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  • 1.
    Axelsson, Patrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Norrlöf, Mikael
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Bayesian Methods for Estimating Tool Position of an Industrial Manipulator2012Inngår i: Proceedings of Reglermöte 2012, 2012Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    State estimation of a flexible industrial manipulator is presented using experimental data. The problem is formulated in a Bayesian framework where the extended Kalman filter and particle filter are used. The filters use the joint positions on the motor side of the gearboxes as well as the acceleration at the end-effector as measurements and estimates the corresponding joint angles on the arm side of the gearboxes. The techniques are verified on a state of the art industrial robot, and it is shown that the use of the acceleration at the end-effector improves the estimates significantly.

  • 2.
    Axelsson, Patrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Norrlöf, Mikael
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Bayesian State Estimation of a Flexible Industrial Robot2011Rapport (Annet vitenskapelig)
    Abstract [en]

    A sensor fusion method for state estimation of a flexible industrial robot is developed. By measuring the acceleration at the end-effector, the accuracy of the arm angular position, as well as the estimated position of the end-effector are improved. The problem is formulated in a Bayesian estimation framework and two solutions are proposed; the extended Kalman filter and the particle filter. In a simulation study on a realistic flexible industrial robot, the angular position performance is shown to be close to the fundamental Cramér-Rao lower bound. The technique is also verified in experiments on an ABB robot, where the dynamic performance of the position for the end-effector is significantly improved.

  • 3.
    Axelsson, Patrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Norrlöf, Mikael
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Bayesian State Estimation of a Flexible Industrial Robot2012Inngår i: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 20, nr 11, s. 1220-1228Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A sensor fusion method for state estimation of a flexible industrial robot is developed. By measuring the acceleration at the end-effector, the accuracy of the arm angular position, as well as the estimated position of the end-effector are improved. The problem is formulated in a Bayesian estimation framework and two solutions are proposed; the extended Kalman filter and the particle filter. In a simulation study on a realistic flexible industrial robot, the angular position performance is shown to be close to the fundamental Cramér-Rao lower bound. The technique is also verified in experiments on an ABB robot, where the dynamic performance of the position for the end-effector is significantly improved.

  • 4.
    Axelsson, Patrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Norrlöf, Mikael
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Estimation-based ILC using Particle Filter with Application to Industrial Manipulators2013Inngår i: Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013, s. 1740-1745Konferansepaper (Fagfellevurdert)
    Abstract [en]

    An estimation-based iterative learning control (ILC) algorithm is applied to a realistic industrial manipulator model. By measuring the acceleration of the end-effector, the arm angular position accuracy is improved when the measurements are fused with motor angle observations. The estimation problem is formulated in a Bayesian estimation framework where three solutions are proposed: one using the extended Kalman filter (EKF), one using the unscented  Kalman filter (UKF), and one using the particle filter (PF).  The estimates are used in an ILC method to improve the accuracy for following a given reference trajectory.  Since the ILC algorithm is repetitive no computational restrictions on the methods apply explicitly. In an extensive Monte Carlo simulation study it is shown that the PF method outperforms the other methods and that the ILC control law is substantially improved using the PF estimate.

  • 5.
    Axelsson, Patrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Norrlöf, Mikael
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Estimation-based Norm-optimal Iterative Learning Control2013Rapport (Annet vitenskapelig)
    Abstract [en]

    The iterative learning control (ILC) method improvesperformance of systems that repeat the same task several times. In this paper the standard norm-optimal ILC control law for linear systems is extended to an estimation-based ILC algorithm where the controlled variables are not directly available as measurements. The proposed ILC algorithm is proven to be stable and gives monotonic convergence of the error. The estimation-based part of the algorithm uses Bayesian estimation techniques such as the Kalman filter. The objective function in the optimisation problem is modified to incorporate not only the mean value of the estimated variable, but also information about the uncertainty of the estimate. It is further shown that for linear time-invariant systems the ILC design is independent of the estimation method. Finally, the concept is extended to non-linear state space models using linearisation techniques, where it is assumed that the full state vector is estimated and used in the ILC algorithm. It is also discussed how the Kullback-Leibler divergence can be used if linearisation cannot be performed. Finally, the proposed solution for non-linear systems is applied and verified in a simulation study with a simplified model of an industrial manipulator system.

  • 6.
    Axelsson, Patrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Norrlöf, Mikael
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Estimation-based Norm-optimal Iterative Learning Control2014Inngår i: Systems & control letters (Print), ISSN 0167-6911, E-ISSN 1872-7956, Vol. 73, s. 76-80Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The norm-optimal iterative learning control (ilc) algorithm for linear systems is extended to an estimation-based norm-optimal ilc  algorithm where the controlled variables are not directly available as measurements. A separation lemma is presented, stating that if a stationary Kalman filter is used for linear time-invariant systems then the ilc  design is independent of the dynamics in the Kalman filter. Furthermore, the objective function in the optimisation problem is modified to incorporate the full probability density function of the error. Utilising the Kullback–Leibler divergence leads to an automatic and intuitive way of tuning the ilc  algorithm. Finally, the concept is extended to non-linear state space models using linearisation techniques, where it is assumed that the full state vector is estimated and used in the ilc  algorithm. Stability and convergence properties for the proposed scheme are also derived.

  • 7.
    Axelsson, Patrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Norrlöf, Mikael
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Tool Position Estimation of a Flexible Industrial Robot using Recursive Bayesian Methods2012Inngår i: Proceedings of the 2012 IEEE International Conference on Robotics and Automation, 2012, s. 5234-5239Konferansepaper (Fagfellevurdert)
    Abstract [en]

    A sensor fusion method for state estimation of a flexible industrial robot is presented. By measuring the acceleration at the end-effector, the accuracy of the arm angular position is improved significantly when these measurements are fused with motor angle observation. The problem is formulated in a Bayesian estimation framework and two solutions are proposed; one using the extended Kalman filter (EKF) and one using the particle filter (PF). The technique is verified on experiments on the ABB IRB4600 robot, where the accelerometer method is showing a significant better dynamic performance, even when model errors are present.

  • 8.
    Axelsson, Patrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Norrlöf, Mikael
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Tool Position Estimation of a Flexible Industrial Robot using Recursive Bayesian Methods2011Rapport (Annet vitenskapelig)
    Abstract [en]

    A sensor fusion method for state estimation of a flexible industrial robot is presented. By measuring the acceleration at the end-effector, the accuracy of the arm angular position is improved significantly when these measurements are fused with motor angle observation. The problem is formulated in a Bayesian estimation framework and two solutions are proposed; one using the extended Kalman filter (EKF) and one using the particle filter (PF). The technique is verified on experiments on the ABB IRB4600 robot, where the accelerometer method is showing a significant better dynamic performance, even when model errors are present.

  • 9.
    Boers, Yvo
    et al.
    THALES, The Netherlands.
    Driessen, Hans
    THALES, The Netherlands.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Torstensson, Johan
    Linköpings universitet.
    Trieb, Mikael
    Linköpings universitet.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    A Track Before Detect Algorithm for Tracking Extended Targets2006Inngår i: IEE Proceedings - Radar Sonar and Navigation, ISSN 1350-2395, E-ISSN 1359-7086, Vol. 153, nr 4, s. 345-351Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    For certain types of sensor-target configurations, a point target model or approach is not suitable and the physical extent of the target is accounted for during processing. An extended target track-before-detect (TBD) algorithm is presented and the performance is compared to an algorithm based on the point target assumption. Simulations illustrate the gain in performance obtained by using the extended target model where a particle filter is used for the TBD implementation.

  • 10.
    Boers, Yvo
    et al.
    THALES, The Netherlands.
    Driessen, Hans
    THALES, The Netherlands.
    Torstensson, Johan
    Linköpings universitet.
    Trieb, Mikael
    Linköpings universitet.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    A Track Before Detect Algorithm for Tracking Extended Targets2005Rapport (Annet vitenskapelig)
    Abstract [en]

    For certain types of sensor-target configurations, a point target model or approach is not suitable and the physical extent of the target is accounted for during processing. An extended target track-before-detect (TBD) algorithm is presented and the performance is compared to an algorithm based on the point target assumption. Simulations illustrate the gain in performance obtained by using the extended target model where a particle filter is used for the TBD implementation.

  • 11.
    Gustafsson, Fredrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gunnarsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Bergman, Niclas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Forsell, Urban
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Jansson, Jonas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Nordlund, Per-Johan
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Particle Filters for Positioning, Navigation and Tracking2002Inngår i: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 50, nr 2, s. 425-437Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A framework for positioning, navigation and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general non-linear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position derivatives, and the particle filter becomes low-dimensional. This is of utmost importance for high-performance real-time applications. Automotive and airborne applications illustrate numerically the advantage over classical Kalman filter based algorithms. Here the use of non-linear models and non-Gaussian noise is the main explanation for the improvement in accuracy. More specifically, we describe how the technique of map matching is used to match an aircraft's elevation profile to a digital elevation map, and a car's horizontal driven path to a street map. In both cases, real-time implementations are available, and tests have shown that the accuracy in both cases is comparable to satellite navigation (as GPS), but with higher integrity. Based on simulations, we also argue how the particle filter can be used for positioning based on cellular phone measurements, for integrated navigation in aircraft, and for target tracking in aircraft and cars. Finally, the particle filter enables a promising solution to the combined task of navigation and tracking, with possible application to airborne hunting and collision avoidance systems in cars.

  • 12.
    Gustafsson, Fredrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Nira Dynamics AB, Linköping, Sweden.
    Generating Dithering Noise for Maximum Likelihood Estimation from Quantized Data2013Inngår i: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 49, nr 2, s. 554-560Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The Quantization Theorem I (QT I) implies that the likelihood function can be reconstructed from quantized sensor observations, given that appropriate dithering noise is added before quantization. We present constructive algorithms to generate such dithering noise. The application to maximum likelihood estimation (mle) is studied in particular. In short, dithering has the same role for amplitude quantization as an anti-alias filter has for sampling, in that it enables perfect reconstruction of the dithered but unquantized signal’s likelihood function. Without dithering, the likelihood function suffers from a kind of aliasing expressed as a counterpart to Poisson’s summation formula which makes the exact mle intractable to compute. With dithering, it is demonstrated that standard mle algorithms can be re-used on a smoothed likelihood function of the original signal, and statistically efficiency is obtained. The implication of dithering to the Cramér–Rao Lower Bound (CRLB) is studied, and illustrative examples are provided.

  • 13.
    Gustafsson, Fredrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Range Estimation using Angle-Only Target Tracking with Particle Filters2001Inngår i: Proceedings of the Third Conference on Computer Science and Systems Engineering, 2001, s. 55-62Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    We consider the recursive state estimation of a maneuverable aircraft using an airborne passive IR-sensor. The main issue addressed in the paper is the range- and velocity estimation using angle-only measurements. In contrast to standard target tracking literature we do not rely on linearized motion models and measurement relations, or on any Gaussian assumptions. Instead, we apply optimal recursive Bayesian filters directly to the nonlinear target model. We present novel sequential simulation based algorithms developed explicitly for the angle-only target tracking problem. These Monte Carlo filters approximate optimal inference by simulating a large number of tracks, or particles. In a simulation study our particle filter approach is compared to a range parameterized extended Kalman filter (RPEKF). Tracking is performed in both Cartesian and modified spherical coordinates (MSC).

  • 14.
    Gustafsson, Fredrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Statistical Results for System Identification based on Quantized Observations2009Inngår i: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 45, nr 12, s. 2794-2801Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    System identification based on quantized observations requires either approximations of the quantization noise, leading to suboptimal algorithms, or dedicated algorithms tailored to the quantization noise properties. This contribution studies fundamental issues in estimation that relate directly to the core methods in system identification. As a first contribution, results from statistical quantization theory are surveyed and applied to both moment calculations (mean, variance etc) and the likelihood function of the measured signal. In particular, the role of adding dithering noise at the sensor is studied. The overall message is that tailored dithering noise can considerably simplify the derivation of optimal estimators. The price for this is a decreased signal to noise ratio, and a second contribution is a detailed study of these effects in terms of the Cramer-Rao lower bound. The common additive uniform noise approximation of quantization is discussed, compared, and interpreted in light of the suggested approaches.

  • 15.
    Gustafsson, Fredrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Orguner, Umut
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Schön, Thomas B.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Skoglar, Per
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, G Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Navigation and Tracking of Road-Bound Vehicles2012Inngår i: Handbook of Intelligent Vehicles / [ed] Eskandarian, Azim, London: Springer, 2012, s. 397-434Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    The Handbook of Intelligent Vehicles provides a complete coverage of the fundamentals, new technologies, and sub-areas essential to the development of intelligent vehicles; it also includes advances made to date, challenges, and future trends. Significant strides in the field have been made to date; however, so far there has been no single book or volume which captures these advances in a comprehensive format, addressing all essential components and subspecialties of intelligent vehicles, as this book does. Since the intended users are engineering practitioners, as well as researchers and graduate students, the book chapters do not only cover fundamentals, methods, and algorithms but also include how software/hardware are implemented, and demonstrate the advances along with their present challenges. Research at both component and systems levels are required to advance the functionality of intelligent vehicles. This volume covers both of these aspects in addition to the fundamentals listed above. 

  • 16.
    Gustafsson, Fredrik
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Schön, Thomas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Nordlund, Per-Johan
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för systemteknik, Reglerteknik.
    State-of-the-Art for the Marginalized Particle Filter2006Inngår i: Proceedings of the 2006 IEEE Nonlinear Statistical Signal Processing Workshop, 2006, s. 172-174Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The marginalized particle filter is a powerful combination of the particle filter and the Kalman filter, which can be used when the underlying model contains a linear substructure subject to Gaussian noise. This paper surveys state of the art for theory and practice.

  • 17.
    Hendeby, Gustaf
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Hol, Jeroen
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    A Graphics Processing Unit Implementation of the Particle Filter2007Inngår i: Proceedings of the 15th European Statistical Signal Processing Conference, European Association for Signal, Speech, and Image Processing , 2007, s. 1639-1643Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Modern graphics cards for computers, and especially their graphics processing units (GPUs), are designed for fast rendering of graphics. In order to achieve this GPUs are equipped with a parallel architecture which can be exploited for general-purpose computing on GPU (GPGPU) as a complement to the central processing unit (CPU). In this paper GPGPU techniques are used to make a parallel GPU implementation of state-of-the-art recursive Bayesian estimation using particle filters (PF). The modifications made to obtain a parallel particle filter, especially for the resampling step, are discussed and the performance of the resulting GPU implementation is compared to one achieved with a traditional CPU implementation. The resulting GPU filter is faster with the same accuracy as the CPU filter for many particles, and it shows how the particle filter can be parallelized.

  • 18.
    Hendeby, Gustaf
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Hol, Jeroen
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    A Graphics Processing Unit Implementation of the Particle Filter2007Rapport (Annet vitenskapelig)
    Abstract [en]

    Modern graphics cards for computers, and especially their graphics processing units (GPUs), are designed for fast rendering of graphics. In order to achieve this GPUs are equipped with a parallel architecture which can be exploited for general-purpose computing on GPU (GPGPU) as a complement to the central processing unit (CPU). In this paper GPGPU techniques are used to make a parallel GPU implementation of state-of-the-art recursive Bayesian estimation using particle filters (PF). The modifications made to obtain a parallel particle filter, especially for the resampling step, are discussed and the performance of the resulting GPU implementation is compared to one achieved with a traditional CPU implementation. The resulting GPU filter is faster with the same accuracy as the CPU filter for many particles, and it shows how the particle filter can be parallelized.

  • 19.
    Hendeby, Gustaf
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Hol, Jeroen
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Graphics Processing Unit Implementation of the Particle Filter2006Rapport (Annet vitenskapelig)
    Abstract [en]

    Modern graphics cards for computers, and especially their graphics processing units (GPUs), are designed for fast rendering of graphics. In order to achieve this GPUs are equipped with a parallel architecture which can be exploited for general-purpose computing on GPU (GPGPU) as a complement to the central processing unit (CPU). In this paper GPGPU techniques are used to make a parallel GPU implementation of state-of-the-art recursive Bayesian estimation using particle filters (PF). The modifications made to obtain a parallel particle filter, especially for the resampling step, are discussed and the performance of the resulting GPU implementation is compared to one achieved with a traditional CPU implementation. The resulting GPU filter is faster with the same accuracy as the CPU filter for many particles, and it shows how the particle filter can be parallelized.

  • 20.
    Hendeby, Gustaf
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gaussian Mixture PHD Filtering with Variable Probability of Detection2014Inngår i: 17th International Conference on Information Fusion (FUSION), 2014, IEEE , 2014, s. 1-7Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The probabilistic hypothesis density (PHD) filter has grown in popularity during the last decade as a way to address the multi-target tracking problem. Several algorithms exist; for instance under linear-Gaussian assumptions, the Gaussian mixture PHD (GM-PHD) filter. This paper extends the GM-PHD filter to the common case with variable probability of detection throughout the tracking volume. This allows for more efficient utilization, e.g., in situations with distance dependent probability of detection or occluded regions. The proposed method avoids previous algorithmic pitfalls that can result in a not well-defined PHD. The method is illustrated and compared to the standard GM-PHD in a simplified multi-target tracking example as well asin a realistic nonlinear underwater sonar simulation application, both demonstrating the effectiveness of the proposed method.

  • 21.
    Hendeby, Gustaf
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Target Tracking Performance Evaluation - A General Software Environment for Filtering2007Inngår i: Proceedings of the 2007 IEEE Aerospace Conference, 2007, s. 1-13Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper, several recursive Bayesian filtering methods for target tracking are discussed. Performance for target tracking problems is usually measured using the second-order moment. For nonlinear or non-Gaussian applications, this measure is not always sufficient. The Kullback divergence is proposed as an alternative to mean square error analysis, and it is extensively used to compare estimated posterior distributions for various applications. The important issue of efficient software development, for nonlinear and non-Gaussian estimation, is also addressed. A new framework in C++ is detailed. Utilizing modern design techniques an object oriented filtering and simulation framework is provided to allow for easy and efficient comparisons of different estimators. The software environment is extensively used in several applications and examples.

  • 22.
    Hendeby, Gustaf
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Target Tracking Performance Evaluation - A General Software Environment for Filtering2007Rapport (Annet vitenskapelig)
    Abstract [en]

    In this paper, several recursive Bayesian filtering methods for target tracking are discussed. Performance for target tracking problems is usually measured using the second-order moment. For nonlinear or non-Gaussian applications, this measure is not always sufficient. The Kullback divergence is proposed as an alternative to mean square error analysis, and it is extensively used to compare estimated posterior distributions for various applications. The important issue of efficient software development, for nonlinear and non-Gaussian estimation, is also addressed. A new framework in C++ is detailed. Utilizing modern design techniques an object oriented filtering and simulation framework is provided to allow for easy and efficient comparisons of different estimators. The software environment is extensively used in several applications and examples.

  • 23.
    Hendeby, Gustaf
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    A New Formulation of the Rao-Blackwellized Particle Filter2007Inngår i: Proceedings of the 14th IEEE/SP Statistical Signal Processing Workshop, 2007, s. 84-88Konferansepaper (Fagfellevurdert)
    Abstract [en]

    For performance gain and efficiency it is important to utilize model structure in particle filtering. Applying Bayes- rule, present linear Gaussian substructure can be efficiently handled by a bank of Kalman filters. This is the standard formulation of the Rao-Blackwellized particle filter (RBPF), by some authors denoted the marginalized particle filter (MPF), and usually presented in a way that makes it hard to implement in an object oriented fashion. This paper discusses how the solution can be rewritten in order to increase the understanding as well as simplify the implementation and reuse of standard filtering components, such as Kalman filter banks and particle filters. Calculations show that the new algorithm is equivalent to the classical formulation, and the new algorithm is exemplified in a target tracking simulation study.

  • 24.
    Hendeby, Gustaf
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    A New Formulation of the Rao-Blackwellized Particle Filter2007Rapport (Annet vitenskapelig)
    Abstract [en]

    For performance gain and efficiency it is important to utilize model structure in particle filtering. Applying Bayes- rule, present linear Gaussian substructure can be efficiently handled by a bank of Kalman filters. This is the standard formulation of the Rao-Blackwellized particle filter (RBPF), by some authors denoted the marginalized particle filter (MPF), and usually presented in a way that makes it hard to implement in an object oriented fashion. This paper discusses how the solution can be rewritten in order to increase the understanding as well as simplify the implementation and reuse of standard filtering components, such as Kalman filter banks and particle filters. Calculations show that the new algorithm is equivalent to the classical formulation, and the new algorithm is exemplified in a target tracking simulation study.

  • 25.
    Hendeby, Gustaf
    et al.
    German Research Centre Artificial Intelligence, Germany.
    Karlsson, Rickard
    NIRA Dynamics AB, Sweden.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Particle Filtering: The Need for Speed2010Inngår i: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, Vol. 2010, nr 181403Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The particle filter (PF) has during the last decade been proposed for a wide range of localization and tracking applications. There is a general need in such embedded system to have a platform for efficient and scalable implementation of the PF. One such platform is the graphics processing unit (GPU), originally aimed to be used for fast rendering of graphics. To achieve this, GPUs are equipped with a parallel architecture which can be exploited for general-purpose computing on GPU (GPGPU) as a complement to the central processing unit (CPU). In this paper, GPGPU techniques are used to make a parallel recursive Bayesian estimation implementation using particle filters. The modifications made to obtain a parallel particle filter, especially for the resampling step, are discussed and the performance of the resulting GPU implementation is compared to the one achieved with a traditional CPU implementation. The comparison is made using a minimal sensor network with bearings-only sensors. The resulting GPU filter, which is the first complete GPU implementation of a PF published to this date, is faster than the CPU filter when many particles are used, maintaining the same accuracy. The parallelization utilizes ideas that can be applicable for other applications.

  • 26.
    Hendeby, Gustaf
    et al.
    German Research Centre for Artificial Intelligence, Germany.
    Karlsson, Rickard
    Swedish Defence Research Agency, Sweden.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    The Rao-Blackwellized Particle Filter: A Filter Bank Implementation2010Inngår i: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, Vol. 2010, nr 724087Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    For computational efficiency, it is important to utilize model structure in particle filtering. One of the most important cases occurs when there exists a linear Gaussian substructure, which can be efficiently handled by Kalman filters. This is the standard formulation of the Rao-Blackwellized particle filter (RBPF). This contribution suggests an alternative formulation of this well-known result that facilitates reuse of standard filtering components and which is also suitable for object-oriented programming. Our RBPF formulation can be seen as a Kalman filter bank with stochastic branching and pruning.

  • 27.
    Hendeby, Gustaf
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gordon, Neil
    DSTO, Australia.
    Performance Issues in Non-Gaussian Filtering Problems2006Inngår i: Proceedings of the 2006 IEEE Nonlinear Statistical Signal Workshop, 2006, s. 65-68Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Performance for many filtering problems is usually measured using the second order moment. For non-Gaussian application this measure is not always sufficient. In the paper the Kullback divergence is extensively used to compare distributions. Several estimation techniques are compared, and methods such as the particle filter are shown to give superior performance over some classical second-order estimators.

  • 28.
    Hendeby, Gustaf
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gordon, Neil
    DSTO, Australia.
    Performance Issues in Non-Gaussian Filtering Problems2006Rapport (Annet vitenskapelig)
    Abstract [en]

    Performance for many filtering problems is usually measured using the second order moment. For non-Gaussian application this measure is not always sufficient. In the paper the Kullback divergence is extensively used to compare distributions. Several estimation techniques are compared, and methods such as the particle filter are shown to give superior performance over some classical second-order estimators.

  • 29.
    Hendeby, Gustaf
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gordon, Neil
    Defence Science and Technology Organisation, Australia.
    Recursive Triangulation Using Bearings-Only Sensors2006Inngår i: Proceedings of the 2006 IEE Seminar on Target Tracking: Algorithms and Applications, Institution of Electrical Engineers (IEE), 2006, s. 3-10Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Recursive triangulation, using a bearings-only sensor, is investigated for a fly-by scenario. In a simulation study, several estimators are compared, fundamental estimation limits are calculated for different measurement noise assumptions. The quality of the estimated state distributions is evaluated.

  • 30.
    Hendeby, Gustaf
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gordon, Neil
    Defence Science and Technology Organisation, Australia.
    Recursive Triangulation Using Bearings-Only Sensors2006Rapport (Annet vitenskapelig)
    Abstract [en]

    Recursive triangulation, using a bearings-only sensor, is investigated for a fly-by scenario. In a simulation study, several estimators are compared, fundamental estimation limits are calculated for different measurement noise assumptions. The quality of the estimated state distributions is evaluated.

  • 31.
    Karlsson, G. Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Integrated Detection and Tracking of Stealthy Targets - A Particle Filter Approach2005Inngår i: Proceedings of the 2005 IEE Signal Processing Solutions for Homeland Security, 2005Konferansepaper (Fagfellevurdert)
    Abstract [en]

    n/a

  • 32.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik. Linköpings universitet, Tekniska högskolan.
    Particle filtering for positioning and tracking applications2005Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    A Bayesian approach to positioning and tracking applications naturally leads to a recursive estimation formulation. The recently invented particle filter provides a numerical solution to the non-tractable recursive Bayesian estimation problem. As an alternative, traditional methods such as the extended Kalman filter. which is based on a linearized model and an assumption on Gaussian noise, yield approximate solutions.

    In many practical applications, signal quantization and algorithmic complexity are fundamental issues. For measurement quantization, estimation performance is analyzed in detail. The algorithmic complexity is addressed for the marginalized particle filter, where the Kalman filter solves a linear subsystem subject to Gaussian noise efficiently.

    The particle filter is adopted to several positioning and tracking applications and compared to traditional approaches. Particularly, the use of external database information to enhance estimation performance is discussed. In parallel, fundamental limits are derived analytically or numerically using the Cramér-Rao lower bound, and the result from estimation studies is compared to the corresponding lower bound. A framework for map-aided positioning at sea is developed, featuring an underwater positioning system using depth information and readings from a sonar sensor and a novel surface navigation system using radar measurements and sea chart information. Bayesian estimation techniques are also used to improve position accuracy for an industrial robot. The bearings-only tracking problem is addressed using Bayesian techniques and map information is used to improve the estimation performance. For multiple-target tracking problems data association is an important issue. A method to incorporate classical association methods when the estimation is based on the particle filter is presented. A real-time implementation of the particle filter as well as hypothesis testing is introduced for a collision avoidance application.

    Delarbeid
    1. Filtering and Estimation for Quantized Sensor Information
    Åpne denne publikasjonen i ny fane eller vindu >>Filtering and Estimation for Quantized Sensor Information
    2005 (engelsk)Rapport (Annet vitenskapelig)
    Abstract [en]

    The implication of quantized sensor information on estimation and filtering problems is studied. The close relation between sampling and quantization theory was earlier reported by Widrow, Kollar and Liu (1996). They proved that perfect reconstruction of the probability density function (pdf) is possible if the characteristic function of the sensor noise pdf is band-limited. These relations are here extended by providing a class of band-limited pdfs, and it is shown that adding such dithering noise is similar to anti-alias filtering in sampling theory. This is followed up by the implications for Maximum Likelihood and Bayesian estimation. The Cramer-Rao lower bound (CRLB) is derivedfor estimation and filtering on quantized data. A particle filter (PF) algorithm that approximates the optimal nonlinear filter is provided, and numerical experiments show that the PF attains the CRLB, while second-order optimal Kalman filter approaches can perform quite bad.

    sted, utgiver, år, opplag, sider
    Linköping: Linköping University Electronic Press, 2005. s. 14
    Serie
    LiTH-ISY-R, ISSN 1400-3902 ; 2674
    Emneord
    Quantization, Estimation, Filtering, Cramér-Rao lower bound
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-56024 (URN)LiTH-ISY-R-2674 (ISRN)
    Tilgjengelig fra: 2010-04-30 Laget: 2010-04-30 Sist oppdatert: 2014-08-12bibliografisk kontrollert
    2. Complexity Analysis of the Marginalized Particle Filter
    Åpne denne publikasjonen i ny fane eller vindu >>Complexity Analysis of the Marginalized Particle Filter
    2005 (engelsk)Inngår i: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 53, nr 11, s. 4408-4411Artikkel i tidsskrift (Fagfellevurdert) Published
    Abstract [en]

    In this paper, the computational complexity of the marginalized particle filter is analyzed and a general method to perform this analysis is given. The key is the introduction of the equivalent flop measure. In an extensive Monte Carlo simulation, different computational aspects are studied and compared with the derived theoretical results.

    sted, utgiver, år, opplag, sider
    IEEE Signal Processing Society, 2005
    Emneord
    Complexity analysis, Kalman filter, Equivalent flop, Marginalized particle filter, Nonlinear estimation
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-11751 (URN)10.1109/TSP.2005.857061 (DOI)
    Tilgjengelig fra: 2008-05-07 Laget: 2008-05-07 Sist oppdatert: 2017-12-13
    3. Bayesian Surface and Underwater Navigation
    Åpne denne publikasjonen i ny fane eller vindu >>Bayesian Surface and Underwater Navigation
    2006 (engelsk)Inngår i: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 54, nr 11, s. 4204-4213Artikkel i tidsskrift (Fagfellevurdert) Published
    Abstract [en]

    A common framework for maritime surface and underwater (UW) map-aided navigation is proposed as a supplement to satellite navigation based on the global positioning system (GPS). The proposed Bayesian navigation method is based on information from a distance measuring equipment (DME) which is compared with the information obtained from various databases. As a solution to the recursive Bayesian navigation problem, the particle filter is proposed. For the described system, the fundamental navigation performance expressed as the Crameacuter-Rao lower bound (CRLB) is analyzed and an analytic solution as a function of the position is derived. Two detailed examples of different navigation applications are discussed: surface navigation using a radar sensor and a digital sea chart and UW navigation using a sonar sensor and a depth database. In extensive Monte Carlo simulations, the performance is shown to be close to the CRLB. The estimation performance for the surface navigation application is in comparison with usual GPS performance. Experimental data are also successfully applied to the UW application.

    Emneord
    Cramér-Rao lower bound, Particle filter, Recursive Bayesian estimation, Sea navigation
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-50088 (URN)10.1109/TSP.2006.881176 (DOI)
    Tilgjengelig fra: 2009-10-11 Laget: 2009-10-11 Sist oppdatert: 2017-12-12
    4. Bayesian State Estimation of a Flexible Industrial Robot
    Åpne denne publikasjonen i ny fane eller vindu >>Bayesian State Estimation of a Flexible Industrial Robot
    2005 (engelsk)Rapport (Annet vitenskapelig)
    Abstract [en]

    A sensor fusion method for state estimation of a flexible industrial robot is developed. By measuring the acceleration at the end-effector, the accuracy of the arm angular position, as well as the estimated position of the end-effector are improved. The problem is formulated in a Bayesian estimation framework and two solutions are proposed; the extended Kalman filter and the particle filter. In a simulation study on a realistic flexible industrial robot, the angular position performance is shown to be close to the fundamental Cramér-Rao lower bound. The technique is also verified in experiments on an ABB robot, where the dynamic performance of the position for the end-effector is significantly improved.

    sted, utgiver, år, opplag, sider
    Linköping: Linköping University Electronic Press, 2005. s. 10
    Serie
    LiTH-ISY-R, ISSN 1400-3902 ; 2677
    Emneord
    Industrial robot, Positioning, Estimation, Particle filter, Extended Kalman filter, Cramér–Rao lower bound
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-56016 (URN)LiTH-ISY-R-2677 (ISRN)
    Prosjekter
    Vinnova Excellence Center LINK-SICSSF project Collaborative Localization
    Forskningsfinansiär
    VinnovaSwedish Foundation for Strategic Research
    Tilgjengelig fra: 2010-04-30 Laget: 2010-04-30 Sist oppdatert: 2014-08-13bibliografisk kontrollert
    5. Recursive Bayesian Estimation: Bearings-Only Applications
    Åpne denne publikasjonen i ny fane eller vindu >>Recursive Bayesian Estimation: Bearings-Only Applications
    2005 (engelsk)Inngår i: IEE Proceedings - Radar Sonar and Navigation, ISSN 1350-2395, E-ISSN 1359-7086, Vol. 152, nr 5, s. 305-313Artikkel i tidsskrift (Fagfellevurdert) Published
    Abstract [en]

    Recursive Bayesian estimation methods are applied to several angle-only applications. Air-to-air passive ranging, in addition to an air-to-sea application with terrain induced constraints, is discussed. The incorporation of terrain information improves estimation performance. The bearings-only problem is also discussed using experimental data from a torpedo, i.e. sea-to-sea with a passive sonar sensor. The Bayesian estimation problem is solved using the particle filter and the marginalised particle filter. For comparison, a filter bank method using range parameterised extended Kalman filters is used.

    Emneord
    Bayes methods, Kalman filters, Passive filters, Recursive estimation, Sonar signal processing, Target tracking
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-29601 (URN)10.1049/ip-rsn:20045073 (DOI)14979 (Lokal ID)14979 (Arkivnummer)14979 (OAI)
    Tilgjengelig fra: 2009-10-09 Laget: 2009-10-09 Sist oppdatert: 2017-12-13
    6. Monte Carlo Data Association for Multiple Target Tracking
    Åpne denne publikasjonen i ny fane eller vindu >>Monte Carlo Data Association for Multiple Target Tracking
    2001 (engelsk)Inngår i: Proceedings of the 2001 IEE International Seminar on Target Tracking: Algorithms and Applications, 2001, s. 13/1-13/5Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    The data association problem occurs in multiple target tracking applications. Since nonlinear and non-Gaussian estimation problems are solved approximately in an optimal way using recursive Monte Carlo methods or particle filters, the association step is crucial for the overall performance. We introduce a Bayesian data association method based on the particle filter idea and joint probabilistic data association (JPDA) hypothesis calculations. A comparison with classical EKF based data association methods such as the nearest neighbor (NN) method and the JPDA method is made. The NN association method is also applied to the particle filter method. Multiple target tracking using particle filtering increases the computational burden, therefore a control structure for the number of samples needed is proposed. A radar target tracking application is used in a simulation study for evaluation.

    Emneord
    Bayes method, Kalman filter, Nonlinear estimation, Radar tracking
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-29618 (URN)10.1049/ic:20010239 (DOI)14997 (Lokal ID)14997 (Arkivnummer)14997 (OAI)
    Konferanse
    IEE International Seminar on Target Tracking: Algorithms and Applications, Enschede, The Netherlands, October, 2001
    Tilgjengelig fra: 2009-10-09 Laget: 2009-10-09 Sist oppdatert: 2013-03-29
    7. Auxiliary Particle Filters for Tracking a Maneuvering Target
    Åpne denne publikasjonen i ny fane eller vindu >>Auxiliary Particle Filters for Tracking a Maneuvering Target
    2000 (engelsk)Inngår i: Proceedings of the 39th IEEE Conference on Decision and Control, IEEE , 2000, s. 3891-3895 vol.4Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    We consider the recursive state estimation of a highly maneuverable target. In contrast to standard target tracking literature we do not rely on linearized motion models and measurement relations, or on any Gaussian assumptions. Instead, we apply optimal recursive Bayesian filters directly to the nonlinear target model. We present novel sequential simulation based algorithms developed explicitly for the maneuvering target tracking problem. These Monte Carlo filters perform optimal inference by simulating a large number of tracks, or particles. Each particle is assigned a probability weight determined by its likelihood. The maina dvantage of our approach is that linearizations and Gaussian assumptions need not be considered. Instead, a nonlinear model is directly used during the prediction and likelihood update. Detailed nonlinear dynamics models and non-Gaussian sensors can therefore be utilized in an optimal manner resulting in high performance gains. In a simulation comparison with current state-of-the-art tracking algorithms we show that our approach yields performance improvements. Moreover, incorporation of physical constraints with sustained optimal performance is straight forward, which is virtually impossible to incorporate for linear Gaussian filters. With the particle filtering approach we advocate these constraints are easily introduced and improve the results.

    sted, utgiver, år, opplag, sider
    IEEE, 2000
    Emneord
    Bayes methods, Filtering theory, Probability, State estimation, Target tracking
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-29619 (URN)10.1109/CDC.2000.912320 (DOI)14998 (Lokal ID)0-7803-6638-7 (ISBN)14998 (Arkivnummer)14998 (OAI)
    Konferanse
    39th IEEE Conference on Decision and Control, Sydney, Australia, 12-15 December, 2000
    Tilgjengelig fra: 2009-10-09 Laget: 2009-10-09 Sist oppdatert: 2014-12-17
    8. Model-Based Statistical Tracking and Decision Making for Collision Avoidance Application
    Åpne denne publikasjonen i ny fane eller vindu >>Model-Based Statistical Tracking and Decision Making for Collision Avoidance Application
    2004 (engelsk)Inngår i: Proceedings of the 2004 American Control Conference, 2004, s. 3435-3440Konferansepaper, Publicerat paper (Fagfellevurdert)
    Abstract [en]

    A growing research topic within the automotive industry is active safety systems. These systems aim at helping the driver avoid or mitigate the consequences of an accident. In this paper a collision mitigation system that performs late braking is discussed. The brake decision is based on estimates from tracking sensors. We use a Bayesian approach, implementing an extended Kalman filter (EKF) and a particle filter to solve the tracking problem. The two filters are compared for different sensor noise distributions in a Monte Carlo simulation study. In particular a bi-modal Gaussian distribution is proposed to model measurement noise for normal driving. For ideal test conditions the noise probability density is derived from experimental data. The brake decision is based on a statistical hypothesis test, where collision risk is measured in terms of required acceleration to avoid collision. The particle filter method handles this test easily. Since the test is not analytically solvable a stochastic integration is performed for the EKF method. Both systems perform well in the simulation study under the assumed sensor accuracy. The particle filter based algorithm is also implemented in a real-time testbed and fulfilled the on-line requirements.

    Emneord
    Bayes methods, Gaussian distribution, Kalman filters, Monte Carlo methods, Automobile industry, braking, Collision avoidance, Decision making, Road safety, Safety systems, Statistical testing, Stochastic processes, Tracking
    HSV kategori
    Identifikatorer
    urn:nbn:se:liu:diva-24139 (URN)3722 (Lokal ID)0-7803-8335-4 (ISBN)3722 (Arkivnummer)3722 (OAI)
    Konferanse
    2004 American Control Conference, Boston, MA, USA, June-July, 2004
    Tilgjengelig fra: 2009-10-07 Laget: 2009-10-07 Sist oppdatert: 2013-08-29
  • 33.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Simulation Based Methods for Target Tracking2002Licentiatavhandling, monografi (Annet vitenskapelig)
    Abstract [en]

    In this thesis we study a Bayesian estimation formulation of the target tracking problem. Traditionally, linear or linearized models are used, where the uncertainty in the sensor and motion models is typically modeled by Gaussian densities. Hence, classical sub-optimal Bayesian methods based on linearized Kalman filters can be used. The sequential Monte Carlo method, or particle filter, provides an approximative solution to the non-linear and non-Gaussian estimation problem. The particle filter approximates the optimal solution, hence it can outperform the Kalman filter in many cases, given sufficient computational resources. A survey over relevant tracking literature is presented including aspects as estimation, data association, sensor fusion and target modeling. In various target tracking related estimation and data association applications, we extend or modify particle filtering algorithms.

    The passive ranging application when only angle information is available is discussed for several problems. In an air-to-sea application it is shown how to incorporate terrain induced constraints using a terrain database. The algorithm is also successfully evaluated on experimental sonar data acquired from a torpedo system.

    In a multi-target data association application a simulation based approach for data association is proposed and compared to classical algorithms for an air-to-air tracking application. Moreover, the number of particles needed in the particle filter is adapted using a control structure to reduce the computational complexity.

  • 34.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Various Topics on Angle-Only Tracking using Particle Filters2002Inngår i: Proceedings of Reglermöte 2002, 2002, s. 220-Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    Angle-only tracking estimates range and range rate from measured angle information by maneuvering the observation platform to gain observability. Traditionally, linear or linearized models are used, where the uncertainty in the sensor and motion models is typically modeled by Gaussian densities. Hence, classical sub-optimal Bayesian methods based on linearized Kalman filters can be used. The sequential Monte Carlo method, or particle filter, provides an approximative solution to the non-linear and non-Gaussian estimation problem. The particle filter approximates the optimal solution, hence it can outperform the Kalman filter in many cases, given sufficient computational resources. In an air-to-sea application it is shown how to incorporate terrain induced constraints using a terrain database. The algorithm is also successfully evaluated on experimental sonar data acquired from a torpedo system.

  • 35.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Bergman, Niclas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Auxiliary Particle Filters for Tracking a Maneuvering Target2000Inngår i: Proceedings of the 39th IEEE Conference on Decision and Control, IEEE , 2000, s. 3891-3895 vol.4Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We consider the recursive state estimation of a highly maneuverable target. In contrast to standard target tracking literature we do not rely on linearized motion models and measurement relations, or on any Gaussian assumptions. Instead, we apply optimal recursive Bayesian filters directly to the nonlinear target model. We present novel sequential simulation based algorithms developed explicitly for the maneuvering target tracking problem. These Monte Carlo filters perform optimal inference by simulating a large number of tracks, or particles. Each particle is assigned a probability weight determined by its likelihood. The maina dvantage of our approach is that linearizations and Gaussian assumptions need not be considered. Instead, a nonlinear model is directly used during the prediction and likelihood update. Detailed nonlinear dynamics models and non-Gaussian sensors can therefore be utilized in an optimal manner resulting in high performance gains. In a simulation comparison with current state-of-the-art tracking algorithms we show that our approach yields performance improvements. Moreover, incorporation of physical constraints with sustained optimal performance is straight forward, which is virtually impossible to incorporate for linear Gaussian filters. With the particle filtering approach we advocate these constraints are easily introduced and improve the results.

  • 36.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Bergman, Niclas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Maneuvering Target Tracking using Auxiliary Particle Filters2000Inngår i: Proceedings of Reglermöte 2000, 2000, s. 278-283Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    We consider the recursive state estimation of a highly maneuverable target. In contrast to standard target tracking literature we do not rely on linearized motion models and measurement relations, or on any Gaussian assumptions. Instead, we apply optimal recursive Bayesian filters directly to the nonlinear target model. We present novel sequential simulation based algorithms developed explicitly for the maneuvering target tracking problem. These Monte Carlo filters perform optimal inference by simulating a large number of independent tracks, or particles. Each particle is assigned a probability weight determined by its likelihood. The main advantage of our approach is that linearizations and Gaussian assumptions need not be considered. Instead, a nonlinear model is directly used during the prediction and likelihood update. Detailed nonlinear dynamics models and non-Gaussian sensors can therefore be utilized in an optimal manner resulting in %high performance gains. In a simulation comparisons with current state-of-the-art tracking algorithms we show that our approach yields performance improvements at a moderately increased computational cost. Moreover, incorporation of physical constraints with sustained optimal performance is straightforward, which is virtually impossible to incorporate for linear Gaussian filters. With the particle filtering approach we advocate these constraints are easily introduced and improve the results substantially.

  • 37.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Bayesian Surface and Underwater Navigation2004Rapport (Annet vitenskapelig)
    Abstract [en]

    A common framework for maritime surface and underwater (UW) map-aided navigation is proposed as a supplement to satellite navigation based on the global positioning system (GPS). The proposed Bayesian navigation method is based on information from a distance measuring equipment (DME) which is compared with the information obtained from various databases. As a solution to the recursive Bayesian navigation problem, the particle filter is proposed. For the described system, the fundamental navigation performance expressed as the Crameacuter-Rao lower bound (CRLB) is analyzed and an analytic solution as a function of the position is derived. Two detailed examples of different navigation applications are discussed: surface navigation using a radar sensor and a digital sea chart and UW navigation using a sonar sensor and a depth database. In extensive Monte Carlo simulations, the performance is shown to be close to the CRLB. The estimation performance for the surface navigation application is in comparison with usual GPS performance. Experimental data are also successfully applied to the UW application.

  • 38.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Bayesian Surface and Underwater Navigation2006Inngår i: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 54, nr 11, s. 4204-4213Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A common framework for maritime surface and underwater (UW) map-aided navigation is proposed as a supplement to satellite navigation based on the global positioning system (GPS). The proposed Bayesian navigation method is based on information from a distance measuring equipment (DME) which is compared with the information obtained from various databases. As a solution to the recursive Bayesian navigation problem, the particle filter is proposed. For the described system, the fundamental navigation performance expressed as the Crameacuter-Rao lower bound (CRLB) is analyzed and an analytic solution as a function of the position is derived. Two detailed examples of different navigation applications are discussed: surface navigation using a radar sensor and a digital sea chart and UW navigation using a sonar sensor and a depth database. In extensive Monte Carlo simulations, the performance is shown to be close to the CRLB. The estimation performance for the surface navigation application is in comparison with usual GPS performance. Experimental data are also successfully applied to the UW application.

  • 39.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Filtering and Estimation for Quantized Sensor Information2005Rapport (Annet vitenskapelig)
    Abstract [en]

    The implication of quantized sensor information on estimation and filtering problems is studied. The close relation between sampling and quantization theory was earlier reported by Widrow, Kollar and Liu (1996). They proved that perfect reconstruction of the probability density function (pdf) is possible if the characteristic function of the sensor noise pdf is band-limited. These relations are here extended by providing a class of band-limited pdfs, and it is shown that adding such dithering noise is similar to anti-alias filtering in sampling theory. This is followed up by the implications for Maximum Likelihood and Bayesian estimation. The Cramer-Rao lower bound (CRLB) is derivedfor estimation and filtering on quantized data. A particle filter (PF) algorithm that approximates the optimal nonlinear filter is provided, and numerical experiments show that the PF attains the CRLB, while second-order optimal Kalman filter approaches can perform quite bad.

  • 40.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    GPS-free Navigation at Sea using Particle Filters2006Inngår i: Proceedings of Reglermöte 2006, 2006Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    A map-aided navigation method for positioning at sea is proposed, as a supplement to satellite navigation based on the global positioning system (GPS). The proposed Bayesian navigation method is based on information from a radar and information from databases. For the described system, the fundamental navigation performance expressed as the Cramér-Rao lower bound (CRLB) is analyzed and an analytic solution as a function of the position is derived. As a solution to the recursive Bayesian navigation problem, the particle filter is proposed. In an extensive Monte Carlo simulation performance equals a GPS system.

  • 41.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Monte Carlo Data Association for Multiple Target Tracking2001Inngår i: Proceedings of the 2001 IEE International Seminar on Target Tracking: Algorithms and Applications, 2001, s. 13/1-13/5Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The data association problem occurs in multiple target tracking applications. Since nonlinear and non-Gaussian estimation problems are solved approximately in an optimal way using recursive Monte Carlo methods or particle filters, the association step is crucial for the overall performance. We introduce a Bayesian data association method based on the particle filter idea and joint probabilistic data association (JPDA) hypothesis calculations. A comparison with classical EKF based data association methods such as the nearest neighbor (NN) method and the JPDA method is made. The NN association method is also applied to the particle filter method. Multiple target tracking using particle filtering increases the computational burden, therefore a control structure for the number of samples needed is proposed. A radar target tracking application is used in a simulation study for evaluation.

  • 42.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Particle Filter for Underwater Terrain Navigation2003Inngår i: Proceedings of the 2003 IEEE Workshop on Statistical Signal Processing, 2003, s. 526-529Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In an earlier contribution we proposed a particle filter for under water (UW) navigation, and applied it to an experimental underwater mental trajectory. Here we focus on performance improvements and analysis. First, the Cramer Rao lower bound (CRLB) along the experimental trajectory is computed, which is only slightly lower than the particle filter estimate after initial transients. Simple rule of thumbs for how performance depends on the map and sensor quality are derived. Second, a more realistic five state model is proposed, and Rao-Blackwellization is applied to decrease computational complexity. Monte-Carlo simiulations on the map demonstrate a performance comparable to the CRLB.

  • 43.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Particle Filter for Underwater Terrain Navigation2003Rapport (Annet vitenskapelig)
    Abstract [en]

    In an earlier contribution we proposed a particle filter for under water (UW) navigation, and applied it to an experimental underwater mental trajectory. Here we focus on performance improvements and analysis. First, the Cramer Rao lower bound (CRLB) along the experimental trajectory is computed, which is only slightly lower than the particle filter estimate after initial transients. Simple rule of thumbs for how performance depends on the map and sensor quality are derived. Second, a more realistic five state model is proposed, and Rao-Blackwellization is applied to decrease computational complexity. Monte-Carlo simiulations on the map demonstrate a performance comparable to the CRLB.

  • 44.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Particle Filtering for Quantized Sensor Information2005Inngår i: Proceedings of the 13th European Signal Processing Conference, 2005, s. 201-204Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The implication of quantized sensor information on filtering problems is studied. The Cramer-Rao lower bound (CRLB) is derived for estimation and filtering on quantized data. A particle filter (PF) algorithm that approximates the optimal nonlinear filter is provided, and numerical experiments show that the PF attains the CRLB, while second-order optimal Kalman filter (KF) approaches can perform quite bad.

  • 45.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Particle Filtering for Quantized Sensor Information2005Rapport (Annet vitenskapelig)
    Abstract [en]

    The implication of quantized sensor information on filtering problems is studied. The Cramer-Rao lower bound (CRLB) is derived for estimation and filtering on quantized data. A particle filter (PF) algorithm that approximates the optimal nonlinear filter is provided, and numerical experiments show that the PF attains the CRLB, while second-order optimal Kalman filter (KF) approaches can perform quite bad.

  • 46.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Range Estimation using Angle-Only Target Tracking with Particle Filters2001Inngår i: Proceedings of the 2001 American Control Conference, 2001, s. 3743-3748 vol.5Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We consider the recursive state estimation of a maneuverable aircraft using an airborne passive IR-sensor. The main issue addressed in the paper is the range- and velocity estimation using angle-only measurements. In contrast to standard target tracking literature we do not rely on linearized motion models and measurement relations, or on any Gaussian assumptions. Instead, we apply optimal recursive Bayesian filters directly to the nonlinear target model. We present novel sequential simulation based algorithms developed explicitly for the angle-only target tracking problem. These Monte Carlo filters approximate optimal inference by simulating a large number of tracks, or particles. In a simulation study our particle filter approach is compared to a range parameterized extended Kalman filter (RPEKF). Tracking is performed in both Cartesian and modified spherical coordinates (MSC).

  • 47.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Recursive Bayesian Estimation: Bearings-Only Applications2005Inngår i: IEE Proceedings - Radar Sonar and Navigation, ISSN 1350-2395, E-ISSN 1359-7086, Vol. 152, nr 5, s. 305-313Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Recursive Bayesian estimation methods are applied to several angle-only applications. Air-to-air passive ranging, in addition to an air-to-sea application with terrain induced constraints, is discussed. The incorporation of terrain information improves estimation performance. The bearings-only problem is also discussed using experimental data from a torpedo, i.e. sea-to-sea with a passive sonar sensor. The Bayesian estimation problem is solved using the particle filter and the marginalised particle filter. For comparison, a filter bank method using range parameterised extended Kalman filters is used.

  • 48.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Institutionen för systemteknik, Reglerteknik.
    The Future of Automotive Localization Algorithms: Available, reliable, and scalable localization: Anywhere and anytime2017Inngår i: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 34, nr 2, s. 60-69Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Most navigation systems today rely on global navigation satellite systems (gnss), including in cars. With support from odometry and inertial sensors, this is a sufficiently accurate and robust solution, but there are future demands. Autonomous cars require higher accuracy and integrity. Using the car as a sensor probe for road conditions in cloud-based services also sets other kind of requirements. The concept of the Internet of Things requires stand-alone solutions without access to vehicle data. Our vision is a future with both invehicle localization algorithms and after-market products, where the position is computed with high accuracy in gnss-denied environments. We present a localization approach based on a prior that vehicles spend the most time on the road, with the odometer as the primary input. When wheel speeds are not available, we present an approach solely based on inertial sensors, which also can be used as a speedometer. The map information is included in a Bayesian setting using the particle filter (PF) rather than standard map matching. In extensive experiments, the performance without gnss is shown to have basically the same quality as utilizing a gnss sensor. Several topics are treated: virtual measurements, dead reckoning, inertial sensor information, indoor positioning, off-road driving, and multilevel positioning.

  • 49.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Using the Particle Filter as Mitigation to GPS Vulnerability for Navigation at Sea2005Inngår i: Proceedings of the 13th IEEE/SP Workshop on Statistical Signal Processing, 2005, s. 1270-1273Konferansepaper (Fagfellevurdert)
    Abstract [en]

    A map-aided navigation method for positioning at sea is proposed, as a supplement to satellite navigation based on the global positioning system (GPS). The proposed Bayesian navigation method is based on information from a radar and information from databases. For the described system, the fundamental navigation performance expressed as the Cramér-Rao lower bound (CRLB) is analyzed and an analytic solution as a function of the position is derived. As a solution to the recursive Bayesian navigation problem, the particle filter is proposed. In an extensive Monte Carlo simulation performance equals a GPS system.

  • 50.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Using the Particle Filter as Mitigation to GPS Vulnerability for Navigation at Sea2005Rapport (Annet vitenskapelig)
    Abstract [en]

    A map-aided navigation method for positioning at sea is proposed, as a supplement to satellite navigation based on the global positioning system (GPS). The proposed Bayesian navigation method is based on information from a radar and information from databases. For the described system, the fundamental navigation performance expressed as the Cramér-Rao lower bound (CRLB) is analyzed and an analytic solution as a function of the position is derived. As a solution to the recursive Bayesian navigation problem, the particle filter is proposed. In an extensive Monte Carlo simulation performance equals a GPS system.

12 1 - 50 of 85
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