A Weight-Based Cutoff Resampling Method for Accelerated Particle Filtering

Abstract

The particle filter is known to be a powerful tool for the estimation of moving targets guided by nonlinear dynamics and sensors. The filter, however, is known to suffer from degeneracy — a feature of one particle gathering all the weight, thus causing the filter to completely diverge. Degeneracy problems become more evident when the sensors are accurate and/or the target maneuvers greatly. The resampling step in the particle filter is critical because it avoids degeneracy of particles by eliminating the wasteful use of particles that do not contribute to the posterior probability density function. The conventional resampling methods, despite being unbiased in approximating the posterior density, involve exhaustive and sequential communication within the particles and thus are computationally expensive. Hence conventional resampling is a major bottleneck for fast implementation of particle filters for real-time tracking. In this paper, we propose a new approach of filtering that requires resampling of only a minimum number of the most important particles that contribute to the posterior density. Minimizing the resampling operation to over a few important particles substantially accelerates the filtering process. We show the merits of the proposed method via simulations using a nonlinear example.