A novel multi-sensor multiple model particle filter

The large amount of calculation always severely restricts the application domain expansion of particle filter, a novel multi-sensor multiple model particle filtering algorithm based on particle weight optimization is proposed. In the multiple model particle filter framework, the optimization method of particle weight is realized by the extraction and utilization of redundancy and complementary information from latest multi-sensor observations. Due to weaken the adverse influence of random observations noise, the stability and reliability is effective improved. The theoretical analysis and experimental results show that the new algorithm can improve the filter precision but also lessens computational burden in nonlinear system estimation with multi-sensor multiple model characteristic.