Extended Target Tracking using a Gaussian-Mixture PHD Filter

Date

2012-10-01

Author

Granstrom, Karl
Lundquist, Christian
Orguner, Umut

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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. Suitable remedies are given to handle spatially close targets and target occlusion.

Subject Keywords

Filtering theory, Gaussian processes, Target tracking

URI

https://hdl.handle.net/11511/43877

Journal

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS

DOI

https://doi.org/10.1109/taes.2012.6324703

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

K. Granstrom, C. Lundquist, and U. Orguner, “Extended Target Tracking using a Gaussian-Mixture PHD Filter,” IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, pp. 3268–3286, 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43877.