Title :
Von Mises Mixture PHD Filter
Author :
Markovic, Ivan ; Cesic, Josip ; Petrovic, Ivan
Author_Institution :
Dept. of Control & Comput. Eng., Univ. of Zagreb, Zagreb, Croatia
Abstract :
This letter deals with the problem of tracking multiple targets on the unit circle, a problem that arises whenever the state and the sensor measurements are circular, i.e. angular-only, random variables. To tackle this problem, we propose a novel mixture approximation of the probability hypothesis density filter based on the von Mises distribution, thus constructing a method that globally captures the non-Euclidean nature of the state and the measurement space. We derive a closed-form recursion of the filter and apply principled approximations where necessary. We compared the performance of the proposed filter with the Gaussian mixture probability hypothesis density filter on a synthetic dataset of 100 randomly generated multitarget trajectory examples corrupted with noise and clutter, and on the PETS2009 dataset. We achieved respectively a decrease of 10.5% and 2.8% in the optimal subpattern assignment metric (notably 16.9% and 10.8% in the localization component).
Keywords :
approximation theory; filtering theory; mixture models; target tracking; PETS2009 dataset; Von Mises mixture PHD filter; angular-only random variables; circular random variables; closed-form recursion; mixture approximation; multiple targets tracking; principled approximations; probability hypothesis density filter; sensor measurements; subpattern assignment metric; unit circle; von Mises distribution; Approximation methods; Clutter; Density measurement; Noise; Random variables; Signal processing algorithms; Target tracking; Directional statistics; multitarget tracking; probability hypothesis density filter; von Mises distribution;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2015.2472962