Particle filter algorithm based on particle weight optimization in uncertain measurement

In order to effectively measure the particle weight in uncertain measurement, a novel particle filter algorithm based on particle weight optimization is proposed. In this algorithm, first, the redundancy and complementary information among particles is fully extracted by constructing and solving the confidence distance and the confidence matrix, and a new consistency weight to measure the mutual support degree among particles is presented. Then, the weight balance factor is used to combine the cost reference weight with the consistency weight for reasonably optimizing the particle weight. The proposed algorithm not only makes full use of the information of the particles set at the current time, but also avoids the adverse effect due to the error of prior statistical information, which improves the stability and reliability of particle weight measurement. Theoretical and simulated results show that the proposed algorithm is effective.