Box-Particle Labeled Multi-Bernoulli Filter for Multiple Extended Target Tracking

Abstract

This paper focuses on real-time tracking of multiple extended targets in clutter based on labeled multi-Bernoulli filter. To address this problem, a novel approach is proposed within the recently presented box-particle framework. Unlike the traditional point-particle approach, the measurements of extended targets are modeled as interval measurements in this work, and the corresponding likelihood function is given based on interval analysis. Then, labeled multi-Bernoulli recursion for extended targets is implemented by box particles, referred to as BP-LMB filter. Furthermore, BP-MM-LMB filter is proposed to better accommodate the uncertainty of target dynamics by integrating the BP-LMB filter with interacting multiple models (IMM) algorithm. Simulations demonstrate that the proposed approach can significantly reduce the number of particles and well track multiple extended targets with less runtime.