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  • 51.
    Amirijoo, Mehdi
    et al.
    Linköping University, Department of Computer and Information Science, RTSLAB - Real-Time Systems Laboratory. Linköping University, The Institute of Technology.
    Hansson, Jörgen
    Linköping University, Department of Computer and Information Science, RTSLAB - Real-Time Systems Laboratory. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Son, Sang H.
    University of Virginia, VA, USA.
    Enhancing Feedback Control Scheduling Performance by On-line Quantification and Suppression of Measurement Disturbance2005In: Proceedings of the 11th IEEE Real-Time and Embedded Technology and Applications Symposium, 2005, p. 2-11Conference paper (Refereed)
    Abstract [en]

    In the control of continuous and physical systems, the controlled system is sampled sufficiently fast to capture the system dynamics. In general, this property cannot be applied to the control of computer systems as the measured variables are often computed over a data set, e.g., deadline miss ratio. In this paper we quantize the disturbance present in the measured variable as a function of the sampling period and we propose a measurement disturbance suppressive control structure. The experiments we have carried out show that a controller using the proposed control structure outperforms a traditional control structure with regard to performance reliability and adaptation.

  • 52.
    Amirijoo, Mehdi
    et al.
    Linköping University, Department of Computer and Information Science, RTSLAB - Real-Time Systems Laboratory. Linköping University, The Institute of Technology.
    Hansson, Jörgen
    Linköping University, Department of Computer and Information Science, RTSLAB - Real-Time Systems Laboratory. Linköping University, The Institute of Technology.
    Son, Sang H.
    University of Virginia, USA.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Experimental Evaluation of Linear Time-Invariant Models for Feedback Performance Control in Real-Time Systems2007In: Real-time systems, ISSN 0922-6443, E-ISSN 1573-1383, Vol. 35, no 3, p. 209-238Article in journal (Refereed)
    Abstract [en]

    In recent years a new class of soft real-time applications operating in unpredictable environments has emerged. Typical for these applications is that neither the resource requirements nor the arrival rates of service requests are known or available a priori. It has been shown that feedback control is very effective to support the specified performance of dynamic systems that are both resource insufficient and exhibit unpredictable workloads. To efficiently use feedback control scheduling it is necessary to have a model that adequately describes the behavior of the system. In this paper we experimentally evaluate the accuracy of four linear time-invariant models used in the design of feedback controllers. We introduce a model (DYN) that captures additional system dynamics, which a previously published model (STA) fails to include. The accuracy of the models are evaluated by validating the models with regard to measured data from the controlled system and through a set of experiments where we evaluate the performance of a set of feedback control schedulers tuned using these models. From our evaluations we conclude that second order models (e.g., DYN) are more accurate than first order models (e.g. STA). Further we show that controllers tuned using second order models perform better than controllers tuned using first order models.

  • 53.
    Andersen, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Khoshfetrat Pakazad, Sina
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Rantzer, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Distributed Robust Stability Analysis of Interconnected Uncertain Systems2012In: Proceedings of the 51st IEEE Conference on Decision and Control, 2012, p. 1548-1553Conference paper (Refereed)
    Abstract [en]

    This paper considers robust stability analysis of a large network of interconnected uncertain systems. To avoid analyzing the entire network as a single large, lumped system, we model the network interconnections with integral quadratic constraints. This approach yields a sparse linear matrix inequality which can be decomposed into a set of smaller, coupled linear matrix inequalities. This allows us to solve the analysis problem efficiently and in a distributed manner. We also show that the decomposed problem is equivalent to the original robustness analysis problem, and hence our method does not introduce additional conservativeness.

  • 54.
    Andersen, Martin S.
    et al.
    Technical University of Denmark, Lyngby, Denmark .
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Vandenberghe, Lieven
    University of California (UCLA), Los Angeles, CA, USA.
    Reduced-Complexity Semidefinite Relaxations of Optimal Power Flow Problems2014In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 29, no 4, p. 1855-1863Article in journal (Refereed)
    Abstract [en]

    We propose a new method for generating semidefinite relaxations of optimal power flow problems. The method is based on chordal conversion techniques: by dropping some equality constraints in the conversion, we obtain semidefinite relaxations that are computationally cheaper, but potentially weaker, than the standard semidefinite relaxation. Our numerical results show that the new relaxations often produce the same results as the standard semidefinite relaxation, but at a lower computational cost.

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  • 55.
    Andersen, Martin S.
    et al.
    Technical University of Denmark, Lyngby, Denmark.
    Khoshfetrat Pakazad, Sina
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Rantzer, Anders
    Lund University, Sweden.
    Robust stability analysis of sparsely interconnected uncertain systems2014In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 59, no 8, p. 2151-2156Article in journal (Refereed)
    Abstract [en]

    In this paper, we consider robust stability analysis of large-scale sparsely interconnected uncertain systems. By modeling the interconnections among the subsystems with integral quadratic constraints, we show that robust stability analysis of such systems can be performed by solving a set of sparse linear matrix inequalities. We also show that a sparse formulation of the analysis problem is equivalent to the classical formulation of the robustness analysis problem and hence does not introduce any additional conservativeness. The sparse formulation of the analysis problem allows us to apply methods that rely on efficient sparse factorization techniques, and our numerical results illustrate the effectiveness of this approach compared to methods that are based on the standard formulation of the analysis problem.

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  • 56.
    Anderson, Sören
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On Optimal Dimension Reduction for Sensor Array Signal Processing1993In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 30, no 2, p. 245-256Article in journal (Refereed)
    Abstract [en]

    The computational complexity for direction-of-arrival estimation using sensor arrays increases very rapidly with the number of sensors in the array. One way to lower the amount of computations is to employ some kind of reduction of the data dimension. This is usually accomplished by employing linear transformations for mapping full dimension data into a lower dimensional space. Different approaches for selecting these transformations have been proposed. In this paper, a transformation matrix is derived that makes it possible to theoretically attain the full-dimension Cramér-Rao bound also in the reduced space. A bound on the dimension of the reduced data set is given, above which it is always possible to obtain the same accuracy for the estimates of the source localizations, using the lower-dimension data, as that achievable by using the full dimension data. Furthermore, a method is devised for designing the transformation matrix. Numerical examples, using this design method, are presented, where the achievable performance of the (optimal) Weighted Subspace Fitting method with full dimension data is compared to the performance obtained with reduced dimension data. The problem of estimating parameters of sinusoidal signals from noisy data is also addressed by a direct application of the results derived herein.

  • 57.
    Andersson, Amanda
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control.
    Näsholm, Elin
    Linköping University, Department of Electrical Engineering, Automatic Control.
    Fast Real-Time MPC for Fighter Aircraft2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The main topic of this thesis is model predictive control (MPC) of an unstable fighter aircraft. When flying it is important to be able to reach, but not exceed the aircraft limitations and to consider the physical boundaries on the control signals. MPC is a method for controlling a system while considering constraints on states and control signals by formulating it as an optimization problem. The drawback with MPC is the computational time needed and because of that, it is primarily developed for systems with a slowly varying dynamics.

    Two different methods are chosen to speed up the process by making simplifications, approximations and exploiting the structure of the problem. The first method is an explicit method, performing most of the calculations offline. By solving the optimization problem for a number of data sets and thereafter training a neural network, it can be treated as a simpler function solved online. The second method is called fast MPC, in this case the entire optimization is done online. It uses Cholesky decomposition, backward-forward substitution and warm start to decrease the complexity and calculation time of the program.

    Both methods perform reference tracking by solving an underdetermined system by minimizing the weighted norm of the control signals. Integral control is also implemented by using a Kalman filter to observe constant disturbances. An implementation was made in MATLAB for a discrete time linear model and in ARES, a simulation tool used at Saab Aeronautics, with a more accurate nonlinear model.

    The result is a neural network function computed in tenth of a millisecond, a time independent of the size of the prediction horizon. The size of the fast MPC problem is however directly affected by the horizon and the computational time will never be as small, but it can be reduced to a couple of milliseconds at the cost of optimality.

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  • 58.
    Andersson, Emma
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Intuitive Mission Handling with Automatic Route Re-planning using Model Predictive Control2012Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The system for mission handling in the Gripen fighter aircraft, and in its ground supporting system, consists for example of ways to plan mission routes, create mission points and validate performed missions. The system is complex and for example, the number of different mission points used increases due to changing demands and needs. This master thesis presents suggestions for improvements and simplifications for the mission handling system, to make it more intuitive and more friendly to use. As a base for the suggestions, interviews with pilots from Saab, TUJAS and FMV have been conducted, this is to obtain opinions and ideas from those using the system and have deep knowledge about it.

    Another possible assistance and improvement is to provide the possibility of on-line automatic re-planning of the mission route in case of obstacles. MPC (Model Predictive Control) has been used to estimate the obstacle’s flight path,and calculate a new route to the next mission point which does not conflict with the estimated enemy’s path. This system has been implemented in Matlab and the concept is demonstrated with different test scenarios where the design parameters (prediction horizon and penalty in the cost function) for the controller are varied, and stationary and moving obstacles are induced.

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  • 59.
    Andersson, Johan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A framework for evaluation of iterative learning control2014Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In many industrial applications robots are used for heavy and repetitive tasks. For these applications iterative learning control (ILC) is a way to capture the repetitive nature and use it to improve some kind of reference tracking.

    In this master thesis a framework has been developed to help a user getting started with ILC. Some hands-on examples are given on how to easily use the framework. The transition from the far more common discrete time domain to the continuous time domain used by the framework is eased by tuning theory. The achievable performance is demonstrated with the help of the built-in plot functions of the framework.

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  • 60.
    Andersson, Magnus
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Experimental Design and Updating of Finite Element Models1997Licentiate thesis, monograph (Other academic)
    Abstract [en]

    This thesis deals with two partly related topics: model updating and actuator/sensor placement concerning finite element (FE) models of large, flexible mechanical structures.

    The importance of accurate dynamical FE models of mechanical structures in, e.g., aviation/aerospace applications are steadily increasing. For instance, a sufficient accurate model may reduce the expenses for ground vibration testing and wind-tunnel experiments substantially. It is therefore of high industrial interest to obtain accurate models of flexible structures. One approach is to improve a parameterized, initial FE model using measurements of the real structure, so-called model updating. For a fast, successful model updating, three requirements must be fulfilled. The model updating must be computationally cheap, which requires an efficient model reduction technique. The cost function describing the deviation between the model output and the measurements must have good convexity properties so that an estimation of the parameters corresponding to the global optimum is likely to be obtained. Finally, the optimization methods must be reliable. A novel mode-pairing free cost function is presented, and together with a proposed general procedure for model updating, a cheap model updating formulation with good parameter estimation properties is obtained.

    Actuator and sensor placement is a part of the experimental design. It is performed in advance of the vibrational experiment in order to ensure high quality measurements. Using a nominal FE model of the structure, an actuator/sensor placement can be made. Actuator/sensor placement tasks are generally discrete, non-convex optimization problems of high complexity. One is therefore restricted to the use of sub-optimal algorithms in order to fulfill time and memory storage requirements. A computationally cheap algorithm for general actuator/sensor placement objectives are proposed. A generalization of an actuator/sensor placement criterion for model updating, and a novel noise-robust actuator placement criterion for experimental modal analysis are proposed.

  • 61.
    Andersson, Magnus
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Model Updating - Identification of Mechanical Structures using Finite Element Models1996In: Proceedings of Reglermöte 1996, 1996, p. 51-55Conference paper (Other academic)
  • 62.
    Andersson, Magnus
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Noise Robust Actuator Placement on Flexible Structures1997Report (Other academic)
    Abstract [en]

    A novel criterion for placement of actuators on flexible mechanical structures is presented. Using simulated "measured modes" obtained from the model, the proposed criterion maximizes the correlation of the measured modes and the normal modes. The measured modes deviate from the normal modes due to damping, measurement noise and process noise. The statistical properties of the criterion are investigated. In simulations the computed actuator locations on a small aircraft-like model shows increased robustness properties against damping, for an acceptable loss of correlation. A computationally cheap actuator placement algorithm is proposed.

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  • 63.
    Andersson, Magnus
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. SAAB AB, Sweden.
    Abrahamsson, Tom
    SAAB AB, Sweden.
    Avoiding Mode Pairing when Updating Finite Element Models1997Report (Other academic)
    Abstract [en]

    Updating nite element models of complex mechanical structures requires some extra considerations. It is stressed that the two most important aspects on updating finite element models are parameter estimation properties and computational expenses. A novel mode-pairing free model updating formulation is found to hav egood parameter estimation properties. The computational expenses are reduced with a semi-fixed modal basis, kept fixed during several iterations.

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  • 64.
    Andersson, Magnus
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Glad, Torkel
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Norrlöf, Mikael
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Simulation and Animation Tool for Studying Multivariable Control2002In: Proceedings of the 15th IFAC World Congress, 2002, p. 1432-1432Conference paper (Refereed)
    Abstract [en]

    A simulation and animation tool for education in multivariable control is presented. The purpose of the tool is to support studies of various aspects of multivariable dynamical systems and design of multivariable feedback control systems. Different ways to use this kind of tool in control education are also presented and discussed.

  • 65.
    Andersson, Maria
    et al.
    Swedish Defence Research Agency, Sweden.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    St-Laurent, Louis
    INO, Canada.
    Prevost, Donald
    INO, Canada.
    Recognition of Anomalous Motion Patterns in Urban Surveillance2013In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 7, no 1, p. 102-110Article in journal (Refereed)
    Abstract [en]

    We investigate the unsupervised K-means clustering and the semi-supervised hidden Markov model (HMM) to automatically detect anomalous motion patterns in groups of people (crowds). Anomalous motion patterns are typically people merging into a dense group, followed by disturbances or threatening situations within the group. The application of K-means clustering and HMM are illustrated with datasets from four surveillance scenarios. The results indicate that by investigating the group of people in a systematic way with different K values, analyze cluster density, cluster quality and changes in cluster shape we can automatically detect anomalous motion patterns. The results correspond well with the events in the datasets. The results also indicate that very accurate detections of the people in the dense group would not be necessary. The clustering and HMM results will be very much the same also with some increased uncertainty in the detections.

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  • 66.
    Andersson, Markus
    Linköping University, Department of Electrical Engineering, Automatic Control.
    Automatic Tuning of Motion Control System for an Autonomous Underwater Vehicle2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The interest for marine research and exploration has increased rapidly during the past decades and autonomous underwater vehicles (AUV) have been found useful in an increased amount of applications. The demand for versatile platform AUVs, able to perform a wide range of tasks, has become apparent. A vital part of an AUV is its motion control system, and an emerging problem for multipurpose AUVs is that the control performance is affected when the vehicle is configured with different payloads for each mission. Instead of having to manually re-tune the control system between missions, a method for automatic tuning of the control system has been developed in this master’s thesis.

    A model-based approach was implemented, where the current vehicle dynamics are identified by performing a sequence of excitation maneuvers, generating informative data. The data is used to estimate model parameters in predetermined model structures, and model-based control design is then used to determine an appropriate tuning of the control system.

    The performance and potential of the suggested approach were evaluated in simulation examples which show that improved control can be obtained by using the developed auto-tuning method. The results are considered to be sufficiently promising to justify implementation and further testing on a real AUV.

    The automatic tuning process is performed prior to a mission and is meant to compensate for dynamic changes introduced between separate missions. However, the AUV dynamics might also change during a mission which requires an adaptive control system. By using the developed automatic tuning process as foundation, the first steps towards an indirect adaptive control approach have been suggested.

    Also, the AUV which was studied in the thesis composed another interesting control problem by being overactuated in yaw control, this because yawing could be achieved by using rudders but also by differential drive of the propellers. As an additional and separate part of the thesis, an approach for using both techniques simultaneously have been proposed.

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    Automatic tuning of motion control system for an AUV
  • 67. Order onlineBuy this publication >>
    Andersson Naesseth, Christian
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Machine learning using approximate inference: Variational and sequential Monte Carlo methods2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubiquitous in our everyday life. The systems we design, and technology we develop, requires us to coherently represent and work with uncertainty in data. Probabilistic models and probabilistic inference gives us a powerful framework for solving this problem. Using this framework, while enticing, results in difficult-to-compute integrals and probabilities when conditioning on the observed data. This means we have a need for approximate inference, methods that solves the problem approximately using a systematic approach. In this thesis we develop new methods for efficient approximate inference in probabilistic models.

    There are generally two approaches to approximate inference, variational methods and Monte Carlo methods. In Monte Carlo methods we use a large number of random samples to approximate the integral of interest. With variational methods, on the other hand, we turn the integration problem into that of an optimization problem. We develop algorithms of both types and bridge the gap between them.

    First, we present a self-contained tutorial to the popular sequential Monte Carlo (SMC) class of methods. Next, we propose new algorithms and applications based on SMC for approximate inference in probabilistic graphical models. We derive nested sequential Monte Carlo, a new algorithm particularly well suited for inference in a large class of high-dimensional probabilistic models. Then, inspired by similar ideas we derive interacting particle Markov chain Monte Carlo to make use of parallelization to speed up approximate inference for universal probabilistic programming languages. After that, we show how we can make use of the rejection sampling process when generating gamma distributed random variables to speed up variational inference. Finally, we bridge the gap between SMC and variational methods by developing variational sequential Monte Carlo, a new flexible family of variational approximations.

    List of papers
    1. Capacity estimation of two-dimensional channels using Sequential Monte Carlo
    Open this publication in new window or tab >>Capacity estimation of two-dimensional channels using Sequential Monte Carlo
    2014 (English)In: 2014 IEEE Information Theory Workshop, 2014, p. 431-435Conference paper, Published paper (Refereed)
    Abstract [en]

    We derive a new Sequential-Monte-Carlo-based algorithm to estimate the capacity of two-dimensional channel models. The focus is on computing the noiseless capacity of the 2-D (1, ∞) run-length limited constrained channel, but the underlying idea is generally applicable. The proposed algorithm is profiled against a state-of-the-art method, yielding more than an order of magnitude improvement in estimation accuracy for a given computation time.

    National Category
    Control Engineering Computer Sciences Probability Theory and Statistics
    Identifiers
    urn:nbn:se:liu:diva-112966 (URN)10.1109/ITW.2014.6970868 (DOI)
    Conference
    Information Theory Workshop
    Available from: 2015-01-06 Created: 2015-01-06 Last updated: 2018-11-09
    2. Sequential Monte Carlo for Graphical Models
    Open this publication in new window or tab >>Sequential Monte Carlo for Graphical Models
    2014 (English)In: Advances in Neural Information Processing Systems, 2014, p. 1862-1870Conference paper, Published paper (Refereed)
    Abstract [en]

    We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM). Via a sequential decomposition of the PGM we find a sequence of auxiliary distributions defined on a monotonically increasing sequence of probability spaces. By targeting these auxiliary distributions using SMC we are able to approximate the full joint distribution defined by the PGM. One of the key merits of the SMC sampler is that it provides an unbiased estimate of the partition function of the model. We also show how it can be used within a particle Markov chain Monte Carlo framework in order to construct high-dimensional block-sampling algorithms for general PGMs.

    National Category
    Computer Sciences Probability Theory and Statistics Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-112967 (URN)
    Conference
    Neural Information Processing Systems (NIPS)
    Available from: 2015-01-06 Created: 2015-01-06 Last updated: 2018-11-09Bibliographically approved
    3. Nested Sequential Monte Carlo Methods
    Open this publication in new window or tab >>Nested Sequential Monte Carlo Methods
    2015 (English)In: Proceedings of The 32nd International Conference on Machine Learning / [ed] Francis Bach, David Blei, Journal of Machine Learning Research (Online) , 2015, Vol. 37, p. 1292-1301Conference paper, Published paper (Refereed)
    Abstract [en]

    We propose nested sequential Monte Carlo (NSMC), a methodology to sample from sequences of probability distributions, even where the random variables are high-dimensional. NSMC generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm. Furthermore, NSMC can in itself be used to produce such properly weighted samples. Consequently, one NSMC sampler can be used to construct an efficient high-dimensional proposal distribution for another NSMC sampler, and this nesting of the algorithm can be done to an arbitrary degree. This allows us to consider complex and high-dimensional models using SMC. We show results that motivate the efficacy of our approach on several filtering problems with dimensions in the order of 100 to 1 000.

    Place, publisher, year, edition, pages
    Journal of Machine Learning Research (Online), 2015
    Series
    JMLR Workshop and Conference Proceedings, ISSN 1938-7228 ; 37
    National Category
    Computer Sciences Control Engineering Probability Theory and Statistics
    Identifiers
    urn:nbn:se:liu:diva-122698 (URN)
    Conference
    32nd International Conference on Machine Learning, Lille, France, 6-11 July, 2015
    Available from: 2015-11-16 Created: 2015-11-16 Last updated: 2018-11-09Bibliographically approved
    4. Interacting Particle Markov Chain Monte Carlo
    Open this publication in new window or tab >>Interacting Particle Markov Chain Monte Carlo
    Show others...
    2016 (English)In: Proceedings of the 33rd International Conference on Machine Learning (ICML), 2016Conference paper, Published paper (Refereed)
    Abstract [en]

    We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers. Like related methods, iPMCMC is a Markov chain Monte Carlo sampler on an extended space. We present empirical results that show significant improvements in mixing rates relative to both non-interacting PMCMC samplers and a single PMCMC sampler with an equivalent memory and computational budget. An additional advantage of the iPMCMC method is that it is suitable for distributed and multi-core architectures.

    Keywords
    Sequential Monte Carlo, Probabilistic programming, parallelisation
    National Category
    Computer Sciences Control Engineering Probability Theory and Statistics
    Identifiers
    urn:nbn:se:liu:diva-130043 (URN)
    Conference
    International Conference on Machine Learning (ICML), New York, USA, June 19-24, 2016
    Projects
    CADICS
    Funder
    Cancer and Allergy Foundation
    Available from: 2016-07-05 Created: 2016-07-05 Last updated: 2018-11-09
    5. Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
    Open this publication in new window or tab >>Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
    2017 (English)In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017Conference paper, Published paper (Refereed)
    Abstract [en]

    Variational inference using the reparameterization trick has enabled large-scale approximate Bayesian inference in complex probabilistic models, leveraging stochastic optimization to sidestep intractable expectations. The reparameterization trick is applicable when we can simulate a random variable by applying a differentiable deterministic function on an auxiliary random variable whose distribution is fixed. For many distributions of interest (such as the gamma or Dirichlet), simulation of random variables relies on acceptance-rejection sampling. The discontinuity introduced by the accept-reject step means that standard reparameterization tricks are not applicable. We propose a new method that lets us leverage reparameterization gradients even when variables are outputs of a acceptance-rejection sampling algorithm. Our approach enables reparameterization on a larger class of variational distributions. In several studies of real and synthetic data, we show that the variance of the estimator of the gradient is significantly lower than other state-of-the-art methods. This leads to faster convergence of stochastic gradient variational inference.

    Series
    Proceedings of Machine Learning Research, ISSN 1938-7228 ; 54
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:liu:diva-152645 (URN)
    Conference
    Artificial Intelligence and Statistics, 20-22 April 2017, Fort Lauderdale, FL, USA
    Available from: 2018-11-09 Created: 2018-11-09 Last updated: 2018-11-21
    6. Variational Sequential Monte Carlo
    Open this publication in new window or tab >>Variational Sequential Monte Carlo
    2018 (English)In: Proceedings of International Conference on Artificial Intelligence and Statistics, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands / [ed] Amos Storkey and Fernando Perez-Cruz, 2018, Vol. 84, p. 968-977Conference paper, Published paper (Refereed)
    Abstract [en]

    Many recent advances in large scale probabilistic inference rely on variational methods. The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior. In this paper we present a new approximating family of distributions, the variational sequential Monte Carlo (VSMC) family, and show how to optimize it in variational inference. VSMC melds variational inference (VI) and sequential Monte Carlo (SMC), providing practitioners with flexible, accurate, and powerful Bayesian inference. The VSMC family is a variational family that can approximate the posterior arbitrarily well, while still allowing for efficient optimization of its parameters. We demonstrate its utility on state space models, stochastic volatility models for financial data, and deep Markov models of brain neural circuits.

    Series
    Proceedings of Machine Learning Research, ISSN 2640-3498 ; 84
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:liu:diva-152646 (URN)
    Conference
    The 21st International Conference on Artificial Intelligence and Statistics, Playa Blanca, Lanzarote, Canary Islands, April 9-11, 2018
    Available from: 2018-11-09 Created: 2018-11-09 Last updated: 2019-11-21Bibliographically approved
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    Machine learning using approximate inference: Variational and sequential Monte Carlo methods
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  • 68.
    Andersson Naesseth, Christian
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nowcasting using Microblog Data2012Independent thesis Basic level (degree of Bachelor), 10,5 credits / 16 HE creditsStudent thesis
    Abstract [en]

    The explosion of information and user generated content made publicly available through the internet has made it possible to develop new ways of inferring interesting phenomena automatically. Some interesting examples are the spread of a contagious disease, earth quake occurrences, rainfall rates, box office results, stock market fluctuations and many many more. To this end a mathematical framework, based on theory from machine learning, has been employed to show how frequencies of relevant keywords in user generated content can estimate daily rainfall rates of different regions in Sweden using microblog data.

    Microblog data are collected using a microblog crawler. Properties of the data and data collection methods are both discussed extensively. In this thesis three different model types are studied for regression, linear and nonlinear parametric models as well as a nonparametric Gaussian process model. Using cross-validation and optimization the relevant parameters of each model are estimated and the model is evaluated on independent test data. All three models show promising results for nowcasting rainfall rates.

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  • 69.
    Andersson Naesseth, Christian
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Vision and Radar Sensor Fusion for Advanced Driver Assistance Systems2013Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The World Health Organization predicts that by the year 2030, road traffic injuries will be one of the top five leading causes of death. Many of these deaths and injuries can be prevented by driving cars properly equipped with state-of-the-art safety and driver assistance systems. Some examples are auto-brake and auto-collision avoidance which are becoming more and more popular on the market today. A recent study by a Swedish insurance company has shown that on roadswith speeds up to 50 km/h an auto-brake system can reduce personal injuries by up to 64 percent. In fact in an estimated 40 percent of crashes, the auto-brake reduced the effects to the degree that no personal injury was sustained.

    It is imperative that these so called Advanced Driver Assistance Systems, to be really effective, have good situational awareness. It is important that they have adequate information of the vehicle’s immediate surroundings. Where are other cars, pedestrians or motorcycles relative to our own vehicle? How fast are they driving and in which lane? How is our own vehicle driving? Are there objects in the way of our own vehicle’s intended path? These and many more questions can be answered by a properly designed system for situational awareness.

    In this thesis we design and evaluate, both quantitatively and qualitatively, sensor fusion algorithms for multi-target tracking. We use a combination of camera and radar information to perform fusion and find relevant objects in a cluttered environment. The combination of these two sensors is very interesting because of their complementary attributes. The radar system has high range resolution but poor bearing resolution. The camera system on the other hand has a very high bearing resolution. This is very promising, with the potential to substantially increase the accuracy of the tracking system compared to just using one of the two. We have also designed algorithms for path prediction and a first threat awareness logic which are both qualitively evaluated.

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  • 70.
    Andersson Naesseth, Christian
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Linderman, Scott
    Columbia University, New York City, New York, United States.
    Ranganath, Rajesh
    New York University, New York City, New York, United States.
    Blei, David
    Columbia University, New York City, New York, United States.
    Variational Sequential Monte Carlo2018In: Proceedings of International Conference on Artificial Intelligence and Statistics, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands / [ed] Amos Storkey and Fernando Perez-Cruz, 2018, Vol. 84, p. 968-977Conference paper (Refereed)
    Abstract [en]

    Many recent advances in large scale probabilistic inference rely on variational methods. The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior. In this paper we present a new approximating family of distributions, the variational sequential Monte Carlo (VSMC) family, and show how to optimize it in variational inference. VSMC melds variational inference (VI) and sequential Monte Carlo (SMC), providing practitioners with flexible, accurate, and powerful Bayesian inference. The VSMC family is a variational family that can approximate the posterior arbitrarily well, while still allowing for efficient optimization of its parameters. We demonstrate its utility on state space models, stochastic volatility models for financial data, and deep Markov models of brain neural circuits.

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    Variational Sequential Monte Carlo
  • 71.
    Andersson Naesseth, Christian
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Lindsten, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Capacity estimation of two-dimensional channels using Sequential Monte Carlo2014In: 2014 IEEE Information Theory Workshop, 2014, p. 431-435Conference paper (Refereed)
    Abstract [en]

    We derive a new Sequential-Monte-Carlo-based algorithm to estimate the capacity of two-dimensional channel models. The focus is on computing the noiseless capacity of the 2-D (1, ∞) run-length limited constrained channel, but the underlying idea is generally applicable. The proposed algorithm is profiled against a state-of-the-art method, yielding more than an order of magnitude improvement in estimation accuracy for a given computation time.

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  • 72.
    Andersson Naesseth, Christian
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Lindsten, Fredrik
    The University of Cambridge, Cambridge, United Kingdom.
    Schön, Thomas
    Uppsala University, Uppsala, Sweden.
    Nested Sequential Monte Carlo Methods2015In: Proceedings of The 32nd International Conference on Machine Learning / [ed] Francis Bach, David Blei, Journal of Machine Learning Research (Online) , 2015, Vol. 37, p. 1292-1301Conference paper (Refereed)
    Abstract [en]

    We propose nested sequential Monte Carlo (NSMC), a methodology to sample from sequences of probability distributions, even where the random variables are high-dimensional. NSMC generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm. Furthermore, NSMC can in itself be used to produce such properly weighted samples. Consequently, one NSMC sampler can be used to construct an efficient high-dimensional proposal distribution for another NSMC sampler, and this nesting of the algorithm can be done to an arbitrary degree. This allows us to consider complex and high-dimensional models using SMC. We show results that motivate the efficacy of our approach on several filtering problems with dimensions in the order of 100 to 1 000.

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  • 73.
    Andersson Naesseth, Christian
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Lindsten, Fredrik
    University of Cambridge, Cambridge, UK.
    Schön, Thomas
    Uppsala University, Uppsala, Sweden.
    Sequential Monte Carlo for Graphical Models2014In: Advances in Neural Information Processing Systems, 2014, p. 1862-1870Conference paper (Refereed)
    Abstract [en]

    We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM). Via a sequential decomposition of the PGM we find a sequence of auxiliary distributions defined on a monotonically increasing sequence of probability spaces. By targeting these auxiliary distributions using SMC we are able to approximate the full joint distribution defined by the PGM. One of the key merits of the SMC sampler is that it provides an unbiased estimate of the partition function of the model. We also show how it can be used within a particle Markov chain Monte Carlo framework in order to construct high-dimensional block-sampling algorithms for general PGMs.

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  • 74.
    Andersson Naesseth, Christian
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Columbia University, USA.
    Ruiz, Francisco
    Columbia University, USA, University of Cambridge, UK.
    Linderman, Scott
    Columbia University, USA.
    Blei, David
    Columbia University, USA.
    Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms2017In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017Conference paper (Refereed)
    Abstract [en]

    Variational inference using the reparameterization trick has enabled large-scale approximate Bayesian inference in complex probabilistic models, leveraging stochastic optimization to sidestep intractable expectations. The reparameterization trick is applicable when we can simulate a random variable by applying a differentiable deterministic function on an auxiliary random variable whose distribution is fixed. For many distributions of interest (such as the gamma or Dirichlet), simulation of random variables relies on acceptance-rejection sampling. The discontinuity introduced by the accept-reject step means that standard reparameterization tricks are not applicable. We propose a new method that lets us leverage reparameterization gradients even when variables are outputs of a acceptance-rejection sampling algorithm. Our approach enables reparameterization on a larger class of variational distributions. In several studies of real and synthetic data, we show that the variance of the estimator of the gradient is significantly lower than other state-of-the-art methods. This leads to faster convergence of stochastic gradient variational inference.

  • 75.
    Andersson, Olov
    et al.
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Ljungqvist, Oskar
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Tiger, Mattias
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Axehill, Daniel
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance2018In: 2018 IEEE Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 4467-4474Conference paper (Refereed)
    Abstract [en]

    A key requirement of autonomous vehicles is the capability to safely navigate in their environment. However, outside of controlled environments, safe navigation is a very difficult problem. In particular, the real-world often contains both complex 3D structure, and dynamic obstacles such as people or other vehicles. Dynamic obstacles are particularly challenging, as a principled solution requires planning trajectories with regard to both vehicle dynamics, and the motion of the obstacles. Additionally, the real-time requirements imposed by obstacle motion, coupled with real-world computational limitations, make classical optimality and completeness guarantees difficult to satisfy. We present a unified optimization-based motion planning and control solution, that can navigate in the presence of both static and dynamic obstacles. By combining optimal and receding-horizon control, with temporal multi-resolution lattices, we can precompute optimal motion primitives, and allow real-time planning of physically-feasible trajectories in complex environments with dynamic obstacles. We demonstrate the framework by solving difficult indoor 3D quadcopter navigation scenarios, where it is necessary to plan in time. Including waiting on, and taking detours around, the motions of other people and quadcopters.

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    Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance
  • 76.
    Andersson, Peter
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Adaptive Forgetting in Recursive Identification through Multiple Models1984In: Analysis and Optimization of Systems: Proceedings of the Sixth International Conference on Analysis and Optimization of Systems, Nice, France, 19-22 June, 1984 / [ed] A. Bensoussan, J. L. Lions, New York: Springer Berlin/Heidelberg, 1984, p. 171-185Chapter in book (Refereed)
    Abstract [en]

    A new recursive identification method, Adaptive Forgetting through Multiple Models — AFMM, is presented and evaluated using computer simulations. AFMM is especially suited for identification of systems with jumping parameters or parameters that change in an irregular fashion. It can be viewed as a particular way of implementing adaptive gains or adaptive forgetting factors for recursive identification. The new method essentialy consists of multiple Recursive Least Squares (RLS) algorithms running in parallel, each with a corresponding weighting factor. The simulations indicate that AFMM is able to track rapidly changing parameters well, and that the method is robust in several respects.

  • 77.
    Andersson, Peter
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Adaptive Forgetting in Recursive Identification through Multiple Models1984Report (Other academic)
    Abstract [en]

    A new recursive identification method, adaptive forgetting through multiple models (AFMM) is presented and evaluated using computer simulations. AFMM is especially suited for identification of systems with jumping or rapidly changing parameters. It can be viewed as a particular way of implementing adaptive gains or adaptive forgetting factors for recursive identification. The new method essentially consists of multiple recursive least-squares (RLS) algorithms running in parallel, each with a corresponding weighting factor. The simulations indicate that AFMM is able to track rapidly changing parameters well, and that the method is robust in several respects.

  • 78.
    Andersson, Peter
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Adaptive Forgetting in Recursive Identification through Multiple Models1983Report (Other academic)
    Abstract [en]

    A new recursive identification method, adaptive forgetting through multiple models (AFMM) is presented and evaluated using computer simulations. AFMM is especially suited for identification of systems with jumping or rapidly changing parameters. It can be viewed as a particular way of implementing adaptive gains or adaptive forgetting factors for recursive identification. The new method essentially consists of multiple recursive least-squares (RLS) algorithms running in parallel, each with a corresponding weighting factor. The simulations indicate that AFMM is able to track rapidly changing parameters well, and that the method is robust in several respects.

  • 79.
    Andersson, Peter
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Adaptive Forgetting in Recursive Identification through Multiple Models1983Report (Other academic)
    Abstract [en]

    A new recursive identification method, adaptive forgetting through multiple models (AFMM) is presented and evaluated using computer simulations. AFMM is especially suited for identification of systems with jumping or rapidly changing parameters. It can be viewed as a particular way of implementing adaptive gains or adaptive forgetting factors for recursive identification. The new method essentially consists of multiple recursive least-squares (RLS) algorithms running in parallel, each with a corresponding weighting factor. The simulations indicate that AFMM is able to track rapidly changing parameters well, and that the method is robust in several respects.

  • 80.
    Andersson, Peter
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Adaptive Forgetting in Recursive Identification through Multiple Models1985In: International Journal of Control, ISSN 0020-7179, E-ISSN 1366-5820, Vol. 42, no 5, p. 1175-1193Article in journal (Refereed)
    Abstract [en]

    A new recursive identification method, adaptive forgetting through multiple models (AFMM) is presented and evaluated using computer simulations. AFMM is especially suited for identification of systems with jumping or rapidly changing parameters. It can be viewed as a particular way of implementing adaptive gains or adaptive forgetting factors for recursive identification. The new method essentially consists of multiple recursive least-squares (RLS) algorithms running in parallel, each with a corresponding weighting factor. The simulations indicate that AFMM is able to track rapidly changing parameters well, and that the method is robust in several respects.

  • 81.
    Andersson, Peter
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Adaptive Forgetting through Multiple Models and Adaptive Control of Car Dynamics1983Licentiate thesis, monograph (Other academic)
    Abstract [en]

    A new recursive identif ication method, Adaptive Forgetting through Multiple Models - AFMM, is presented and evaluated using computer simulations. AFMM is specif ically suited for identification of systems with jumping or rapidly changing parameters. It can be viewed as a particular way of implementing adaptive gains or adaptive forgetting factors for recursive identif ication. The new method essentiallyconsists of multiple Recursive Least Squares (RLS) algorithms running in parallel, each with a corresponding weighting factor. The simulations indicate that AFMM is able to track rapidly changing parameters well, and that the method is robust in several respects.

  • 82.
    Andersson, Peter
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Control of a Diesel Generator with Varying Dynamics: A Comparison of Different Adaptive and Constant Regulators1984Report (Other academic)
  • 83.
    Andersson, Peter
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Test Case for Adaptive Control: Car Steering1981In: Proceedings of the 1981 IFAC Symposium on Theory and Applications of Digital Control, 1981Conference paper (Refereed)
  • 84.
    Andersson, Peter
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ljung, Lennart
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Test Case for Adaptive Control: Car Steering1981Report (Other academic)
  • 85.
    Andersson, Sören
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Asymptotic Analysis of Subspace Methods for Beamspace Direction-of-Arrival Estimation using Large Arrays1992Report (Other academic)
    Abstract [en]

    Asymptotic analysis methods for performance prediction of so-called subspace directionof -arrival estimation methods has been developed earlier, assuming that a large-enough number of array measurements, or snapshots, is collected. This paper also addresses the problem of making performance predictions, but for beamspace-based subspace methods. The novel approach in this paper assumes the number of array elements to be large, while the number of snapshots is arbitrary. The perturbation effect, due to additive sensor noise, on a certain subspace is used for establishing the asymptotic behavior of direction-of-arrival estimates. The asymptotic estimation errors for the estimators resulting from Signal Subspace Fitting methods, such as WSF, and Noise Subspace Fitting (NSF) methods, such as MUSIC and a multi-dimensional counterpart to WSF, are shown to be asymptotically unbiased and normally distributed. Provided that the array response vectors become orthogonal when the number of array elements increases, the NSF methods are shown to give consistent estimates even in the case of fully coherent emitter signals, and the WSF method is shown to be consistent for coherent emitter signals even without this assumption. Comparisons with results for Maximum-Likelihood methods yield conditions for guaranteeing efficiency of the methods. Some simulation examples are also included.

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  • 86.
    Andersson, Sören
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Beamspace Transformation Matrix Design using Numerical Optimization1992Report (Other academic)
    Abstract [en]

    This paper addresses the problem of designing a data-dimension reducing transformation matrix, to be used for beamspace direction-of-arrival (DOA) estimation. The design of the transformation matrix is based on numerical optimization techniques. The criteria to be satisfied is to retain as much as possible of the achievable, optimal estimation accuracy using the non-reduced data-sizes, while also taking into account the sidelobe levels of the beampattern. Comparisons of estimation accuracy and sensitivity to out-of-sector emitters for the design methods considered herein are carried out by means of simulation examples, using the WSF-method for the DOA-estimation. In order to reduce the number of parameters to be optimized, a parametrization of the transformation matrix is made, that utilizes the properties of Householder-reflections.

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  • 87.
    Andersson, Sören
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On Dimension Reduction in Sensor Array Signal Processing1992Doctoral thesis, monograph (Other academic)
    Abstract [en]

    During the last decades, sensor array signal processing has been a very active research area. More recently, relations between many of the proposed methods has been examined. The problem of assessing the estimation accuracy of these methods has also been addressed. Realworld applications of these techniques involves spatial distribution of several sensors to be used for collecting measurements of interesting emitted waveforms. From the measurements, detection and localization as well as estimation of the emitted waveforms can be accomplished. Common examples of applications are radar (electromagnetic waveforms) and sonar (acoustical underwater waveforms).

    Another aspect of array processing that recently has been addressed in the literature is that of dimension reduction, where the data vectors collected at the sensor outputs are reduced in size. This reduction is employed mainly in order to lower the amount of computations necessary for obtaining the parameter-estimates of interest; hut some other improvcments has also been observed. These include, e.g., lower sensitivity to sensor noise correlations and, for some estimation methods, higher resolution capability.

    In this thesis, it is demonstrated how to make the dimension reduction in an optimal fashion, where the optimality is with respect to estimation accuracy. More precisely, an expression to be satisfied by a transformation matrix acting on the sensor outputs is derived , that preserves the optimally achievable estimation accuracy (the Cramer-Rao bound) also in the reduced space. A transformation matrix design method that tries to reduce some unwanted properties of the optimal transformation is also outlined and examined. This method is based on numerical optimization of a particular performance mea.sure, motivated by the insight obtained in the process of finding the optimal transformation.

    l\foreover, an asymptotic analysis is performed, using the reduced data vectors, that examines the estimation accuracy of several estimation methods when a !arge number of sensor elements is used. This analysis is valid for a fairly general transformation matrix, and the methods considered are the Weighted Subspace Fitting (WSF) and Noise Subspace Fitting (NSF) methods, including MUSIC. By employing the optimal transformation matrix, the WSF method is shown to to be efficient, i.e., to attain the Cramer-Rao bound. An examination of the estimation accuracy, compared to that optimally attainable, is performed for the case when the transformation matrix differs from the optimal one. Finally, an application is studied, considering the potential use of sensor arrays in mobile communication systems.

  • 88.
    Andersson, Sören
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On Optimal Dimension Reduction for Sensor Array Signal Processing1991Report (Other academic)
    Abstract [en]

    The computational complexity for direction-of-arrival estimation using sensor arrays increases very rapidly with the number of sensors in the array. One way to lower the amount of computations is to employ some kind of reduction of the data dimension. This is usually accomplished by employing linear transformations for mapping full dimension data into a lower dimensional space. Different approaches for selecting these transformations have been proposed. In this paper, a transformation matrix is derived that makes it possible to theoretically attain the full-dimension Cramér-Rao bound also in the reduced space. A bound on the dimension of the reduced data set is given, above which it is always possible to obtain the same accuracy for the estimates of the source localizations, using the lower-dimension data, as that achievable by using the full dimension data. Furthermore, a method is devised for designing the transformation matrix. Numerical examples, using this design method, are presented, where the achievable performance of the (optimal) Weighted Subspace Fitting method with full dimension data is compared to the performance obtained with reduced dimension data. The problem of estimating parameters of sinusoidal signals from noisy data is also addressed by a direct application of the results derived herein.

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  • 89.
    Andersson, Sören
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On Two-Stage Schemes for Beamspace Direction-of-Arrival Estimation1992Report (Other academic)
    Abstract [en]

    This paper addresses performance analysis of two-stage methods for direction-of-arrival (DOA) estimation. By first finding crude initial DOA-estimates, the idea is to use these estimates for designing a transformation matrix as the first step. Then this matrix is employed for mapping the data to the lower-dimensional beamspace. Different ways for obtaining the first-stage DOA-estimates are discussed and various trade-offs that have to be considered are pointed out. The beamspace version of the Weighted Subspace Fitting (WSF) method is used for obtaining the final DOA estimates. An approximate performance bound is stated that assumes the first-stage DOA-estimates to be close to the true DOAs. For comparing the performance of some different ways to obtain the first-stage DOA-estimate, some numerical examples are included.

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  • 90.
    Andersson, Sören
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Optimal Dimension Reduction for Sensor Array Signal Processing1991In: Proceedings of the 25h Asilomar Conference on Signals, Systems and Computers, 1991, p. 918-922Conference paper (Refereed)
    Abstract [en]

    The computational complexity for direction-of-arrival estimation using sensor arrays increases very rapidly with the number of sensors in the array. One way to lower the amount of computations is to employ some kind of reduction of the data dimension. This is usually accomplished by employing linear transformations for mapping full-dimension data into a lower-dimensional space. In the present work, a transformation matrix is derived, that makes it possible to attain the full-dimension Cramer-Rao bound also in the reduced space. A bound on the dimension of the reduced data set is given, above which it is always possible to obtain the same accuracy for the lower-dimension estimates of the source localizations as that achievable by using the full-dimension data. Furthermore, a method is devised for designing the transformation matrix.

  • 91.
    Andersson, Sören
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Optimal Dimension Reduction for Sensor Array Signal Processing1991Report (Other academic)
    Abstract [en]

    The computational complexity for direction-of-arrival estimation using sensor arrays increases very rapidly with the number of sensors in the array. One way to lower the amount of computations is to employ some kind of reduction of the data dimension. This is usually accomplished by employing linear transformations for mapping full-dimension data into a lower-dimensional space. In the present work, a transformation matrix is derived, that makes it possible to attain the full-dimension Cramer-Rao bound also in the reduced space. A bound on the dimension of the reduced data set is given, above which it is always possible to obtain the same accuracy for the lower-dimension estimates of the source localizations as that achievable by using the full-dimension data. Furthermore, a method is devised for designing the transformation matrix.

  • 92.
    Andersson, Sören
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Reduced Dimension Beam-Space Transformation Design using Optimization1990Report (Other academic)
  • 93.
    Andersson, Sören
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Sensor Array Processing: Application to Mobile Communication Systems and Dimension Reduction1990Licentiate thesis, monograph (Other academic)
  • 94.
    Andersson, Sören
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Millnert, Mille
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Viberg, Mats
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wahlberg, Bo
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Study of Adaptive Arrays for Mobile Communication Systems1991In: Proceedings of the 1991 International Conference on Acoustics, Speech and Signal Processing, 1991, p. 3289-3292Conference paper (Refereed)
    Abstract [en]

    The application of adaptive antenna techniques to increase the channel capacity in mobile radio communication is discussed. Directional sensitivity is obtained by using an antenna array at the base station, possibly both in receiving and transmitting mode. A scheme for separating several signals at the same frequency is proposed. The method is based on high-resolution direction finding following by optimal combination of the antenna outputs. Comparisons to a method based on reference signals are made. Computer simulations are carried out to test the applicability of the technique to scattering scenarios that typically arise in urban areas. The proposed scheme is found to have great potential in rejecting cochannel interference, albeit at the expense of high computational requirements.

  • 95.
    Andersson, Sören
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Millnert, Mille
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Viberg, Mats
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wahlberg, Bo
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Study of Adaptive Arrays for Mobile Communication Systems1990Report (Other academic)
    Abstract [en]

    The application of adaptive antenna techniques to increase the channel capacity in mobile radio communication is discussed. Directional sensitivity is obtained by using an antenna array at the base station, possibly both in receiving and transmitting mode. A scheme for separating several signals at the same frequency is proposed. The method is based on high-resolution direction finding following by optimal combination of the antenna outputs. Comparisons to a method based on reference signals are made. Computer simulations are carried out to test the applicability of the technique to scattering scenarios that typically arise in urban areas. The proposed scheme is found to have great potential in rejecting cochannel interference, albeit at the expense of high computational requirements.

  • 96.
    Andersson, Sören
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Millnert, Mille
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Viberg, Mats
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wahlberg, Bo
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    An Adaptive Array for Mobile Communication Systems1990Report (Other academic)
    Abstract [en]

    The use of adaptive antenna techniques to increase the channel capacity is discussed. Directional sensitivity is obtained by using an antenna array at the base station, possibly both in receiving and transmitting mode. A scheme for separating several signals at the same frequency is proposed. The method is based on high-resolution direction-finding followed by optimal combination of the antenna outputs. Comparison with a method based on reference signals is made. Computer simulations are carried out to test the applicability of the technique to scattering scenarios that typically arise in urban areas. The proposed scheme is found to have great potential in rejecting cochannel interference, albeit at the expense of high computational requirements.

  • 97.
    Andersson, Sören
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Millnert, Mille
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Viberg, Mats
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wahlberg, Bo
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    An Adaptive Array for Mobile Communication Systems1991In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 40, no 1, p. 230-236Article in journal (Refereed)
    Abstract [en]

    The use of adaptive antenna techniques to increase the channel capacity is discussed. Directional sensitivity is obtained by using an antenna array at the base station, possibly both in receiving and transmitting mode. A scheme for separating several signals at the same frequency is proposed. The method is based on high-resolution direction-finding followed by optimal combination of the antenna outputs. Comparison with a method based on reference signals is made. Computer simulations are carried out to test the applicability of the technique to scattering scenarios that typically arise in urban areas. The proposed scheme is found to have great potential in rejecting cochannel interference, albeit at the expense of high computational requirements.

  • 98.
    Andersson, Sören
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nehorai, Arye
    Yale University, USA.
    Analysis of a polarized seismic wave model1994Report (Other academic)
    Abstract [en]

    We present a model for polarized seismic waves where the data are collected by three-component geophone receivers. The model is based on two parameters describing the polarization properties of the waveforms. These parameters are the ellipticity and the orientation angle of the polarization ellipse. The model describes longitudinal waveforms (P-waves) as well as elliptically polarized waves. For the latter waves the direction-of-propagation of the waveform is in the plane spanned by the ellipse's major and minor axes; Rayleigh waves are treated as a special case. We analyze the identifiability of the models and derive the Cramer-Rao and mean-square-angular-error (MSAE) bounds involving one or two three-component geophones.

  • 99.
    Andersson, Sören
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nehorai, Arye
    Yale University, USA.
    Optimal Dimension Reduction for Array Processing: Generalized1993Report (Other academic)
    Abstract [en]

    This correspondence extends previously reported work [1, 2] on the problem, or rather possibility, of achieving optimality of beamspace (BS) array processing, where use is made of dimensionally reduced data vectors. The optimality here is with respect to the best possible element space (ESP) parameter estimation accuracy, i.e., the Cramér-Rao bound.

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    Optimal Dimension Reduction for Array Processing: Generalized
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    FULLTEXT01
  • 100.
    Andersson, Sören
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nehorai, Arye
    Yale University, USA.
    Some Comparisons of Cramer-Rao Bounds for Vector Sensors and Scalar Sensor Arrays for Array Processing1993Report (Other academic)
    Abstract [en]

    The effect from polarization of emitted wave fronts on the parameter estimation accuracy for an array composed only of sensors sensitive to just one polarization direction has not been addressed in the literature this far. Antennas with such characteristics are, e.g., dipole (or scalar) antennas. A vector sensor, on the other hand, is a sensor whose output data consists of, for the electromagnetic case, the complete electric and magnetic fields at the sensor. This paper examines some of the effects on the Cram'er-Rao Bound for the elevation and/or azimuth angles to a single source emitting a polarized (electromagnetic) waveform. Since only one vector sensor is needed for estimation of both azimuth and elevation, it would be of interest to compare the lower parameter estimation error bound resulting from the vector sensor data model to the "ordinary" one, i.e. the data model used for scalar arrays. Such comparisons, both analytically and numerically, are herein made for an acoustic data model, as well as for an electromagnetic measurement model, for some simple scenarios and array configurations.

    Download full text (pdf)
    Some Comparisons of Cramer-Rao Bounds for Vector Sensors and Scalar Sensor Arrays for Array Processing
    Download full text (ps)
    FULLTEXT01
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