Multitarget Filtering With Unknown Clutter Density Using a Bootstrap GMCPHD Filter

Author

Beard, Michael ; Ba-Tuong Vo ; Ba-Ngu Vo

Author_Institution

Defence Sci. & Technol. Organ., Rockingham, WA, Australia

Volume

20

Issue

4

fYear

2013

fDate

Apr-13

Firstpage

323

Lastpage

326

Abstract

It was recently demonstrated that the Gaussian Mixture Cardinalised Probability Hypothesis Density (GMCPHD) filter can be used when the clutter density is unknown. Here we examine the performance of this filter, and as one would expect, it does not do as well as the conventional GMCPHD with matched clutter density. To improve the performance, we propose a bootstrap filtering scheme, and demonstrate by simulations on a bearings-only multitarget filtering scenario, that it is capable of performing almost as well as the matched GMCPHD filter.

Keywords

Gaussian processes; clutter; direction-of-arrival estimation; matched filters; probability; statistical analysis; Gaussian mixture cardinalised probability hypothesis density; bearings-only multitarget filtering; bootstrap filtering scheme; matched GMCPHD filter; matched clutter density; unknown clutter density; Australia; Clutter; Educational institutions; Noise measurement; Personal digital assistants; Time measurement; Vectors; Adaptive filtering; clutter rate estimation; multitarget filtering;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2013.2244594

Filename

6451122