A particle filter algorithm based on SSUKF

As an important nonlinear filter theory, the particle filter(PF) is a heated issue in domestic and foreign researches. The option of importance density and resampling are the key steps of particle filter algorithm. The application of UKF algorithm based on SSUT to create the importance probability density function(PDF), with the particle swarm optimization(PSO), can form a new algorithm of particle filter(PSO-SSUPF). PSO can make the particles move to high likelihood area before the weights updating. Consequently, sample impoverishment can be restrained to some extent. With the SSUT cutting down the number of sigma points, the efficiency of the algorithm can be considerably improved in the condition of ensuring the precision being similar with standard UPF, and its performance is confirmed with the simulation.