The Set IMMJPDA filter for multitarget tracking
Conference contribution, 2011

The Set JPDA (SJPDA) filter is a recently developed multi-target tracking filter that utilizes the relation between the density of a random finite set and the ordinary density of a state vector to improve on the Joint Probabilistic Data Association (JPDA) filter. One advantage to the filter is the improved accuracy of the Gaussian approximations of the JPDA, which results in avoidance of track coalescence. Another advantage is an improved estimation accuracy in terms of a measure which disregards target identity. In this paper we extend the filter to also consider multiple motion models. As a basis for the extension we use the Interacting Multiple Model (IMM) algorithm. We derive three alternative filters that we jointly refer to as Set IMMJPDA (SIMMJPDA). They are based on two alternative descriptions of the IMMJPDA filter. In the paper, we also present simulation results for a two-target tracking scenario, which show improved tracking performance for the Set IMMJPDA filter when evaluated with a measure that disregards target identity.

JPDA

finite set statistics

multiple models

IMM

Target tracking

estimation

SJPDA

Author

Daniel Svensson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

David Crouse

Lennart Svensson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Marco Guerriero

P. Willett

Proceedings of SPIE: Signal Processing, Sensor Fusion, and Target Recognition XX. Conference on Signal Processing, Sensor Fusion, and Target Recognition XX Orlando, FL, APR 25-27, 2011

0277-786X (ISSN)

Vol. 8050

Areas of Advance

Information and Communication Technology

Transport

Subject Categories

Probability Theory and Statistics

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1117/12.886937

ISBN

978-0-81948-624-0

More information

Created

10/8/2017