Extended target tracking with a cardinalized probability hypothesis density filter

Date

2011-07-08

Author

Orguner, Umut
Granström, Karl

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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 already been derived by Mahler and a Gaussian mixture implementation has been proposed recently. This work relaxes the Poisson assumptions of the extended target PHD filter in target and measurement numbers to achieve better estimation performance. A Gaussian mixture implementation is described. The early results using real data from a laser sensor confirm that the sensitivity of the number of targets in the extended target PHD filter can be avoided with the added flexibility of the extended target CPHD filter

Subject Keywords

Cardinalized, CPHD, Extended targets, Gaussian mixture, Laser, Multiple target tracking, PHD, Probability hypothesis density, Random sets

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=80052544386&origin=inward
https://hdl.handle.net/11511/87210
https://ieeexplore.ieee.org/abstract/document/5977726

Conference Name

14th International Conference on Information Fusion, Fusion 2011

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Extended Target Tracking using a Gaussian-Mixture PHD Filter Granstrom, Karl; Lundquist, Christian; Orguner, Umut (2012-10-01) This paper presents a Gaussian-mixture (GM) implementation of the probability hypothesis density (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. S...
An Extended Target CPHD Filter and a Gamma Gaussian Inverse Wishart Implementation Lundquist, Christian; Granstrom, Karl; Orguner, Umut (Institute of Electrical and Electronics Engineers (IEEE), 2013-06-01) 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 implementat...
Extended Target Tracking Using Gaussian Processes Wahlström, Niklas; Özkan, Emre (2015-08-15) In this paper, we propose using Gaussian processes to track an extended object or group of objects, that generates multiple measurements at each scan. The shape and the kinematics of the object are simultaneously estimated, and the shape is learned online via a Gaussian process. The proposed algorithm is capable of tracking different objects with different shapes within the same surveillance region. The shape of the object is expressed analytically, with well-defined confidence intervals, which can be used ...
A PHD Filter for Tracking Multiple Extended Targets Using Random Matrices Granstrom, Karl; Orguner, Umut (2012-11-01) This paper presents a random set based approach to tracking of an unknown number of extended targets, in the presence of clutter measurements and missed detections, where the targets' extensions are modeled as random matrices. For this purpose, the random matrix framework developed recently by Koch et al. is adapted into the extended target PHD framework, resulting in the Gaussian inverse Wishart PHD (GIW-PHD) filter. A suitable multiple target likelihood is derived, and the main filter recursion is present...
Gaussian mixture PHD filter for multi-target tracking using passive doppler-only measurements Guldogan, Mehmet B.; Orguner, Umut; Gustafsson, Fredrik (2012-05-17) In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using a passive sensor network. Non-cooperative transmissions from illuminators of opportunity like GSM base stations, FM radio transmitters or digital broadcasters are exploited by non-directional separately located Doppler measuring sensors. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target s...

Citation Formats

U. Orguner and K. Granström, “Extended target tracking with a cardinalized probability hypothesis density filter,” presented at the 14th International Conference on Information Fusion, Fusion 2011, Chicago, IL; United States, 2011, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=80052544386&origin=inward.