ADAPTIVE PARTICLE SAMPLING AND RESAMPLING IN PARALLEL/DISTRIBUTED PARTICLE FILTERS

Particle filters have been widely used in estimating the states of dynamic systems by using Bayesian interference and stochastic sampling techniques. Parallel computing techniques were introduced to improve the performance of sequential particle filters with multiple processing units (PUs). However, the unavoidable communications between Pus, lower the performance. The hybrid and adaptive resampling algorithms were proposed to improve the performance of parallel/distributed particle filters by reducing the communication costs without loss of estimation accuracy. In this paper, we propose an adaptive sampling and resampling technique in particle filters. In the proposed algorithm, the number of particle is dynamically adjustable based on the model convergence. As a result, less particles will be used if the current convergence is good and more particles will be used if the convergence is getting bad. The experimental results show the improved performance by using less particles and reducing the communication cost compared with other algorithms.