Target Tracking Using Particle Filters With Support Vector Regression

Abstract

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper presents a numerical Bayesian approach for the direction-of-arrival (DOA) tracking of multiple targets using a linear and passive sensor array. In this paper, support vector regression (SVR) method is employed, together with particle filters (PFs), to obtain an effective proposed distribution utilizing observed phenomena to propose a new sample. Two PF algorithms are presented: One is based on SVR for a large sample set, and the other is based on sequential SVR for a small sample set. The simulation results present the superiority of the proposed method while considering a small sample set and show that it is also competitive when a large sample set is considered. </para>