Two linear complexity particle filters capable of maintaining target label probabilities for targets in close proximity
Paper in proceedings, 2012

In this work, we introduce two particle filters of linear complexity in the number of particles that take distinct approaches to solving the problem of tracking two targets in close proximity. We operate in the regime in which measurements do not discriminate between targets and hence uncertainties in the labeling of the tracks arise. For simplicity, we limit our study to the two target case for which there are only two possible associations between targets and tracks. The proposed Approximate Set Particle Filter (ASPF) introduces some approximations but has similar complexity and still provides much more accurate descriptions of the posterior uncertainties compared to standard particle filters. The fast Forward Filter Unlabeled Backward Simulator (fast FFUBSi) employs a smoothing technique based on rejection sampling for the calculation of target label probabilities. Simulations show that neither particle filter suffers from track coalescence (when outputting MMOSPA estimates) and both calculate correct target label probabilities.

linear complexity

Particle filter

target labels

Author

R. Georgescu

University of Connecticut

P. Willett

University of Connecticut

Lennart Svensson

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

M. Morelande

University of Melbourne

15th International Conference on Information Fusion, FUSION 2012. Singapore, 7 - 12 September 2012

2370-2377

Subject Categories

Signal Processing

ISBN

978-098244385-9