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Tracking of extended objects and group targets using random matrices - A new approach

Tracking of extended objects and group targets using random ...

引用

11th International Conference on Information fusion, fusion 2008

作者： Feldmann, Michael Fränken, Dietrich D-53343 Wachtberg Germany Data Fusion Algorithms and Software EADS Deutschland GmbH D-89077 Ulm Germany

ISBN: (纸本)9783000248832

The task of tracking extended objects or (partly) unresolvable group targets raises new challenges for both data association and track maintenance. Extended objects may give rise to more than one detection per opportunity where the scattering centers may vary from scan to scan. On the other end, group targets (i. e., a number of closely spaced targets moving in a coordinated fashion) often will not cause as many detections as there are individual targets in the group due to limited sensor resolution capabilities. In both cases, tracking and data association under the one target-one detection assumption are no longer applicable. This paper deals with the problem of maintaining a track for an extended object or group target with varying number of detections. Herein, object extension is represented by a random symmetric positive definite matrix. A recently published Bayesian approach to tackling this problem is analyzed and discussed. From there, a new approach is derived that is expected to overcome some of the weaknesses the Bayesian approach suffers from in certain applications.

关键词： Target tracking

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Missed detection problems in the cardinalized probability hypothesis density filter

Missed detection problems in the cardinalized probability hy...

引用

International Conference on Information fusion

作者： M. Ulmke D. Franken M. Schmidt Department Sensor Data and Information Fusion FGAN-FKIE Wachtberg Germany Data Fusion Algorithms & Software EADS Deutschland GmbH Ulm Germany

The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with varying target number in clutter. In the present work, it is shown that a missed detection in one part of the field of view has a significant effect on the probability hypothesis density (PHD) arbitrarily far apart from the missed detection. In the case of zero false alarm rate, this effect is particularly pronounced and can be calculated by solving the CPHD filter equations analytically. While the CPHD filter update of the total cardinality distribution is exact, the local target number estimate close to the missed detection is artificially strongly reduced. A first ad-hoc approach towards a ldquolocallyrdquo cardinalized PHD filter for reducing this deficiency is presented and discussed.

关键词： Target tracking Equations Filtering algorithms Bayesian methods Surveillance Clutter Information filters

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Tracking of extended objects and group targets using random matrices — a new approach

Tracking of extended objects and group targets using random ...

引用

International Conference on Information fusion

作者： Michael Feldmann Dietrich Franken FGAN Research Institute for Communication Information Processing and Ergonomics Wachtberg Germany Data Fusion Algorithms & Software EADS Deutschland GmbH Ulm Germany

关键词： Target tracking Bayesian methods Kalman filters Kinematics Equations Noise Filtering algorithms

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The sliced Gaussian mixture filter for efficient nonlinear estimation

The sliced Gaussian mixture filter for efficient nonlinear e...

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International Conference on Information fusion

作者： Vesa Klumpp Felix Sawo Uwe D. Hanebeck Dietrich Franken Intelligent Sensor-Actuator-Systems Laboratory Institute of Computer Science and Engineering Universität Karlsruhe Germany Data Fusion Algorithms and Software EADS Deutschland GmbH Ulm Germany

This paper addresses the efficient state estimation for mixed linear/nonlinear dynamic systems with noisy measurements. Based on a novel density representation - sliced Gaussian mixture density - the decomposition into a (conditionally) linear and nonlinear estimation problem is derived. The systematic approximation procedure minimizing a certain distance measure allows the derivation of (close to) optimal and deterministic estimation results. This leads to high-quality representations of the measurement-conditioned density of the states and, hence, to an overall more efficient estimation process. The performance of the proposed estimator is compared to state-of-the-art estimators, like the well-known marginalized particle filter.

关键词： Approximation methods Estimation Particle filters Function approximation Particle measurements Atmospheric measurements Nonlinear dynamical systems

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Consistent unbiased linear filtering with polar measurements

Consistent unbiased linear filtering with polar measurements

引用

fusion 2007 - 2007 10th International Conference on Information fusion

作者： Fränken, Dietrich Data Fusion Algorithms and Software EADS Deutschland GmbH D-89077 Ulm Germany

ISBN: (纸本)0662478304

The problem of tracking objects moving in Cartesian space with sensors delivering polar measurements has been under investigation of several researchers for quite some time now. Different proposals for using measurement conversion techniques in combination with a linear Kalman filter have been made in order to reduce the range bias that shows up in the filter estimates when a Cartesian pseudo-measurement is created from the polar measurements by applying the respective conversion formulae in combination with a corresponding linearized form of the measurement error covariance matrix. It turns out that the actual behavior of these different approaches strongly depends on the specific situation under consideration where observed effects range from a truly (approximate) suppression of conversion bias up to an even increased bias. In this paper, a systematic approach to analyzing the bias effects of measurement conversion in certain typical tracking situations is presented. Starting from this approach, a new measurement conversion technique is proposed that yields consistent unbiased estimates in these cases.

关键词： Kalman filters

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Consistent unbiased linear filtering with polar measurements

Consistent unbiased linear filtering with polar measurements

引用

International Conference on Information fusion

作者： Dietrich Franken Data Fusion Algorithms & Software EADS Deutschland GmbH Ulm Germany

ISBN: (纸本)9780662478300;0662478304

关键词： Maximum likelihood detection Covariance matrix Measurement errors software measurement Time measurement Proposals Yield estimation Particle measurements Noise measurement software algorithms

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Some results on linear unbiased filtering with polar measurements

Some results on linear unbiased filtering with polar measure...

引用

2006 IEEE International Conference on Multisensor fusion and Integration for Intelligent Systems, MFI

作者： Fränken, Dietrich Data Fusion Algorithms and Software EADS Deutschland GmbH D-89077 Ulm Germany

ISBN: (纸本)1424405661

The problem of tracking objects moving in Cartesian space with sensors delivering polar measurements has been under investigation of several researchers for quite some time now. Different proposals for using measurement conversion techniques in combination with a linear Kalman filter have been made. As one possible alternative approach, an (approximate) best linear unbiased estimator (BLUE) has been proposed. In this paper, some of these approaches are reinvestigated by means of a common representation form for all covered techniques (that is, including the BLUE filter) that helps analyzing and understanding the general behavior of these estimators. Some noteworthy results are presented. It will be argued that the BLUE filter in its original form may be prone to yielding indefinite estimation error variance matrices and that later variants of this filter do not exhibit this behavior. A new initialization method for these filters will be derived. © 2006 IEEE.

关键词： Kalman filters

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Some Results on Linear Unbiased Filtering with Polar Measurements

Some Results on Linear Unbiased Filtering with Polar Measure...

引用

International Conference on Multisensor fusion and Integration for Intelligent Systems (MFI)

作者： Dietrich Franken Data Fusion Algorithms & Software EADS Deutschland GmbH Ulm Germany

The problem of tracking objects moving in Cartesian space with sensors delivering polar measurements has been under investigation of several researchers for quite some time now. Different proposals for using measurement conversion techniques in combination with a linear Kalman filter have been made. As one possible alternative approach, an (approximate) best linear unbiased estimator (BLUE) has been proposed. In this paper, some of these approaches are reinvestigated by means of a common representation form for all covered techniques (that is, including the BLUE filter) that helps analyzing and understanding the general behavior of these estimators. Some noteworthy results are presented. It will be argued that the BLUE filter in its original form may be prone to yielding indefinite estimation error variance matrices and that later variants of this filter do not exhibit this behavior. A new initialization method for these filters will be derived

关键词： Nonlinear filters Filtering Covariance matrix Measurement errors Intelligent sensors software measurement Time measurement Proposals Estimation error Particle measurements

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