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Lundquist, Christian
Publications (10 of 40) Show all publications
Granström, K., Lundquist, C., Gustafsson, F. & Orguner, U. (2014). Random Set Methods: Estimation of Multiple Extended Objects. IEEE robotics & automation magazine, 21(2), 73-82
Open this publication in new window or tab >>Random Set Methods: Estimation of Multiple Extended Objects
2014 (English)In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 21, no 2, p. 73-82Article in journal (Refereed) Published
Abstract [en]

Random set based methods have provided a rigorous Bayesian framework and have been used extensively in the last decade for point object estimation. In this paper, we emphasize that the same methodology offers an equally powerful approach to estimation of so called extended objects, i.e., objects that result in multiple detections on the sensor side. Building upon the analogy between Bayesian state estimation of a single object and random finite set estimation for multiple objects, we give a tutorial on random set methods with an emphasis on multiple extended object estimation. The capabilities are illustrated on a simple yet insightful real life example with laser range data containing several occlusions.

Place, publisher, year, edition, pages
IEEE Robotics and Automation Society, 2014
National Category
Control Engineering Signal Processing
Identifiers
urn:nbn:se:liu:diva-105530 (URN)10.1109/MRA.2013.2283185 (DOI)000337124700012 ()
Projects
CADICSCUAS
Funder
Linnaeus research environment CADICS
Available from: 2014-03-26 Created: 2014-03-26 Last updated: 2017-12-05Bibliographically approved
Lundquist, C., Karlsson, R., Özkan, E. & Gustafsson, F. (2014). Tire Radii Estimation Using a Marginalized Particle Filter. IEEE transactions on intelligent transportation systems (Print), 15(2), 663-672
Open this publication in new window or tab >>Tire Radii Estimation Using a Marginalized Particle Filter
2014 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 15, no 2, p. 663-672Article in journal (Refereed) Published
Abstract [en]

In this paper, the measurements of individual wheel speeds and the absolute position from a global positioning system are used for high-precision estimation of vehicle tire radii. The radii deviation from its nominal value is modeled as a Gaussian random variable and included as noise components in a simple vehicle motion model. The novelty lies in a Bayesian approach to estimate online both the state vector and the parameters representing the process noise statistics using a marginalized particle filter (MPF). Field tests show that the absolute radius can be estimated with submillimeter accuracy. The approach is tested in accordance with regulation 64 of the United Nations Economic Commission for Europe on a large data set (22 tests, using two vehicles and 12 different tire sets), where tire deflations are successfully detected, with high robustness, i.e., no false alarms. The proposed MPF approach outperforms common Kalman-filter-based methods used for joint state and parameter estimation when compared with respect to accuracy and robustness.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2014
Keywords
Conjugate prior; marginalized particle filter (MPF); noise parameter estimation; tire radius
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-106514 (URN)10.1109/TITS.2013.2284930 (DOI)000334584800019 ()
Available from: 2014-05-12 Created: 2014-05-09 Last updated: 2017-12-05
Lundquist, C., Granström, K. & Orguner, U. (2013). An Extended Target CPHD Filter and a Gamma Gaussian Inverse Wishart Implementation. IEEE Journal on Selected Topics in Signal Processing, 7(3), 472-483
Open this publication in new window or tab >>An Extended Target CPHD Filter and a Gamma Gaussian Inverse Wishart Implementation
2013 (English)In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 7, no 3, p. 472-483Article in journal (Refereed) Published
Abstract [en]

This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) filter for such targets has been derived by Mahler, and different implementations have been proposed recently. To achieve better estimation performance this work relaxes the Poisson assumptions of the extended target PHD filter in target and measurement numbers. A gamma Gaussian inverse Wishart mixture implementation, which is capable of estimating the target extents and measurement rates as well as the kinematic state of the target, is proposed, and it is compared to its PHD counterpart in a simulation study. The results clearly show that the CPHD filter has a more robust cardinality estimate leading to smaller OSPA errors, which confirms that the extended target CPHD filter inherits the properties of its point target counterpart.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2013
Keywords
Cardinalized, CPHD, Extended targets, Inverse Wishart, Multiple target tracking, Probability hypothesis density, PHD, Random matrices, Random sets
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-94596 (URN)10.1109/JSTSP.2013.2245632 (DOI)000319275500010 ()
Projects
CADICSCUAS
Funder
Swedish Research CouncilSwedish Foundation for Strategic Research
Note

Funding Agencies|Swedish Research Council under the Linnaeus Center (CADICS)||Swedish Research Council|621-2010-4301|Swedish Foundation for Strategic Research||

Available from: 2013-06-27 Created: 2013-06-27 Last updated: 2017-12-06Bibliographically approved
Lundquist, C., Skoglund, M., Granström, K. & Glad, T. (2013). Insights from Implementing a System for Peer-Review. IEEE Transactions on Education, 56(3), 261-267
Open this publication in new window or tab >>Insights from Implementing a System for Peer-Review
2013 (English)In: IEEE Transactions on Education, ISSN 0018-9359, E-ISSN 1557-9638, Vol. 56, no 3, p. 261-267Article in journal (Refereed) Published
Abstract [en]

Courses at the Master’s level in automatic control and signal processing cover mathematical theories and algorithms for control, estimation, and filtering. However, giving students practical experience in how to use these algorithms is also an important part of these courses. A goal is that the students should not only be able to understand and derive these algorithms, but also be able to apply them to real-life technical problems. The latter is achieved by assigning more time to the laboratory tutorials and designing them in such a way that the exercises are open for interpretation; an example of this would be giving the students more freedom to decide how to acquire the data needed to solve the given exercises.The students are asked to hand in a laboratory report in which they describe how they solved the exercises. This paper presents a double-blind peer-review process for laboratory reports, introduced at the Department of Electrical Engineering, Linköping University, Sweden. A survey was administered to students, and the results are summarized in this paper. Also discussed are the teachers’ experiences of peer review and of how students perform later in their education in writing their Master’s theses.

Keywords
Critical thinking, laboratory work, peer assessment, student learning, peer review, student self-assessment, team-based projects
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-96893 (URN)10.1109/TE.2012.2211876 (DOI)000322657200002 ()
Available from: 2013-08-28 Created: 2013-08-28 Last updated: 2017-12-06
Özkan, E., Smidl, V., Saha, S., Lundquist, C. & Gustafsson, F. (2013). Marginalized Adaptive Particle Filtering for Nonlinear Models with Unknown Time-Varying Noise Parameters. Automatica, 49(6), 1566-1575
Open this publication in new window or tab >>Marginalized Adaptive Particle Filtering for Nonlinear Models with Unknown Time-Varying Noise Parameters
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2013 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 49, no 6, p. 1566-1575Article in journal (Refereed) Published
Abstract [en]

Knowledge of the noise distribution is typically crucial for the state estimation of general state-space models. However, properties of the noise process are often unknown in the majority of practical applications. The distribution of the noise may also be non-stationary or state dependent and that prevents the use of off-line tuning methods. For linear Gaussian models, Adaptive Kalman filters (AKF) estimate unknown parameters in the noise distributions jointly with the state. For nonlinear models, we provide a Bayesian solution for the estimation of the noise distributions in the exponential family, leading to a marginalized adaptive particle filter (MAPF) where the noise parameters are updated using finite dimensional sufficient statistics for each particle. The time evolution model for the noise parameters is defined implicitly as a Kullback-Leibler norm constraint on the time variability, leading to an exponential forgetting mechanism operating on the sufficient statistics. Many existing methods are based on the standard approach of augmenting the state with the unknown variables and attempting to solve the resulting filtering problem. The MAPF is significantly more computationally efficient than a comparable particle filter that runs on the full augmented state. Further, the MAPF can handle sensor and actuator offsets as unknown means in the noise distributions, avoiding the standard approach of augmenting the state with such offsets. We illustrate the MAPF on first a standard example, and then on a tire radius estimation problem on real data.

Place, publisher, year, edition, pages
Elsevier, 2013
Keywords
Unknown noise statistics, Adaptive filtering, Marginalized particle filter, Bayesian conjugate prior
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-94600 (URN)10.1016/j.automatica.2013.02.046 (DOI)000319540500005 ()
Funder
Swedish Research Council
Available from: 2013-06-27 Created: 2013-06-27 Last updated: 2017-12-06
Granström, K. & Lundquist, C. (2013). On the Use of Multiple Measurement Models for Extended Target Tracking. In: Proceedings of the 16th International Conference on Information Fusion: . Paper presented at 16th International Conference on Information Fusion, 9-12 Juli 2013, Istanbul, Turkey (pp. 1534-1541).
Open this publication in new window or tab >>On the Use of Multiple Measurement Models for Extended Target Tracking
2013 (English)In: Proceedings of the 16th International Conference on Information Fusion, 2013, p. 1534-1541Conference paper, Published paper (Refereed)
Abstract [en]

This paper considers extended targets that have constant extension shapes, but generate measurements whose appearance can change abruptly. The problem is approached using multiple measurement models, where each model corresponds to a measurement appearance mode. Mode transitions are modeled as dependent on the extended target kinematic state, and a multiple model extended target PHD filter is used to handle multiple targets with multiple appearance modes. The extended target tracking is evaluated using real world data where a laser range sensor is used to track multiple bicycles.

National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-96890 (URN)000341370000205 ()978-605-86311-1-3 (ISBN)
Conference
16th International Conference on Information Fusion, 9-12 Juli 2013, Istanbul, Turkey
Projects
CADICSCUAS
Available from: 2013-08-28 Created: 2013-08-28 Last updated: 2014-10-14Bibliographically approved
Granström, K., Lundquist, C. & Orguner, U. (2012). Extended Target Tracking Using a Gaussian-Mixture PHD Filter. IEEE Transactions on Aerospace and Electronic Systems, 48(4), 3268-3286
Open this publication in new window or tab >>Extended Target Tracking Using a Gaussian-Mixture PHD Filter
2012 (English)In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 48, no 4, p. 3268-3286Article in journal (Refereed) Published
Abstract [en]

This paper presents a Gaussian-mixture implementation of the phd filter for tracking extended targets. The exact filter requires processing of all possible measurement set partitions, which is generally infeasible to implement. A method is proposed for limiting the number of considered partitions and possible alternatives are discussed. The implementation is used on simulated data and in experiments with real laser data, and the advantage of the filter is illustrated. Suitable remedies are given to handle spatially close targets and target occlusion.

Keywords
Target tracking, Extended target, PHD filter, Random set, Gaussian-mixture, Laser sensor
National Category
Signal Processing Control Engineering
Identifiers
urn:nbn:se:liu:diva-71866 (URN)10.1109/TAES.2012.6324703 (DOI)000309865600030 ()
Projects
CADICSETTCUAS
Funder
Swedish Foundation for Strategic Research Swedish Research Council
Available from: 2012-10-01 Created: 2011-11-08 Last updated: 2017-12-08Bibliographically approved
Ardeshiri, T., Orguner, U., Lundquist, C. & Schön, T. (2012). On mixture reduction for multiple target tracking. In: : . Paper presented at Information Fusion (FUSION), 2012 15th International Conference on.
Open this publication in new window or tab >>On mixture reduction for multiple target tracking
2012 (English)Conference paper, Published paper (Refereed)
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-100233 (URN)
Conference
Information Fusion (FUSION), 2012 15th International Conference on
Available from: 2013-10-31 Created: 2013-10-31 Last updated: 2013-12-19Bibliographically approved
Lundquist, C., Schön, T. & Gustafsson, F. (2012). Situational Awareness and Road Prediction for Trajectory Control Applications. In: Azim Eskandarian (Ed.), Handbook of Intelligent Vehicles: (pp. 365-396). Springer London
Open this publication in new window or tab >>Situational Awareness and Road Prediction for Trajectory Control Applications
2012 (English)In: Handbook of Intelligent Vehicles / [ed] Azim Eskandarian, Springer London, 2012, p. 365-396Chapter in book (Refereed)
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.

Place, publisher, year, edition, pages
Springer London, 2012
Keywords
Engineering, Artificial intelligence, Automotive Engineering, Control, Robotics, Mechatronics
National Category
Control Engineering Signal Processing
Identifiers
urn:nbn:se:liu:diva-71660 (URN)10.1007/978-0-85729-085-4_15 (DOI)978-0-85729-084-7 (ISBN)978-0-85729-085-4 (ISBN)
Funder
Swedish Research CouncilSwedish Foundation for Strategic Research
Available from: 2011-11-08 Created: 2011-10-27 Last updated: 2014-11-28Bibliographically approved
Özkan, E., Lundquist, C. & Gustafsson, F. (2011). A Bayesian Approach to Jointly Estimate Tire Radii and Vehicle Trajectory. In: Proceedings of the International IEEE Conference on Intelligent Transportation Systems. Paper presented at 14th International IEEE Conference on Intelligent Transportation Systems, Washington, DC, 5-7 Oct. 2011 (pp. 1-6). Washington DC, USA: IEEE conference proceedings
Open this publication in new window or tab >>A Bayesian Approach to Jointly Estimate Tire Radii and Vehicle Trajectory
2011 (English)In: Proceedings of the International IEEE Conference on Intelligent Transportation Systems, Washington DC, USA: IEEE conference proceedings, 2011, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]

High-precision estimation of vehicle tire radii is considered, based on measurements on individual wheel speeds and absolute position from a global navigation satellite system (GNSS). The wheel speed measurements are subject to noise with time-varying covariance that depends mainly on the road surface. The novelty lies in a Bayesian approach to estimate online the time-varying radii and noise parameters using a marginalized particle filter, where no model approximations are needed such as in previously proposed algorithms based on the extended Kalman filter. Field tests show that the absolute radius can be estimated with millimeter accuracy, while the relative wheel radius on one axle is estimated with submillimeter accuracy.

Place, publisher, year, edition, pages
Washington DC, USA: IEEE conference proceedings, 2011
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-72977 (URN)10.1109/ITSC.2011.6082980 (DOI)978-1-4577-2198-4 (ISBN)
Conference
14th International IEEE Conference on Intelligent Transportation Systems, Washington, DC, 5-7 Oct. 2011
Projects
VR - ETT
Available from: 2011-12-16 Created: 2011-12-13 Last updated: 2011-12-27Bibliographically approved
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