Title :
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
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;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2013.2244594