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A Gaussian mixture PHD filter for extended target tracking
Date
2010-07-29
Author
Granström, Karl
Lundquist, Christian
Orguner, Umut
Metadata
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In extended target tracking, targets potentially produce more than one measurement per time step. Multiple extended targets are therefore usually hard to track, due to the resulting complex data association. The main contribution of this paper is the implementation of a Probability Hypothesis Density ( phd) filter for tracking of multiple extended targets. A general modification of the phd filter to handle extended targets has been presented recently by Mahler, and the novelty in this work lies in the realisation of a Gaussian mixture phd filter for extended targets. Furthermore, we propose a method to easily partition the measurements into a number of subsets, each of which is supposed to contain measurements that all stem from the same source. The method is illustrated in simulation examples, and the advantage of the implemented extended target phd filter is shown in a comparison with a standard phd filter.
Subject Keywords
Estimation
,
Extended targets
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79952432784&origin=inward
https://hdl.handle.net/11511/86072
DOI
https://doi.org/10.1109/icif.2010.5711885
Conference Name
13th Conference on Information Fusion, Fusion 2010, 26 - 29 Temmuz 2010
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
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K. Granström, C. Lundquist, and U. Orguner, “A Gaussian mixture PHD filter for extended target tracking,” presented at the 13th Conference on Information Fusion, Fusion 2010, 26 - 29 Temmuz 2010, Edinburgh, İngiltere, 2010, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79952432784&origin=inward.