Target tracking technology based on improved swarm intelligent particle filter via PauTa criterion migration optimization

Particle filter (PF) has been confirmed to be an effective method for nonlinear non-Gaussian systems. However, it is difficult to calculate the importance density function and particle degradation (particle simplification) is prone to occur. For the importance density function selection problem, an improved echolocation algorithm is used to approximate the optimal importance density function and optimize the particle distribution to make it closer to the high-likelihood region. For the problem of resampling, an improved dolphin swarm optimization algorithm has been developed to replace traditional resampling methods, which enhances the diversity of particles and further optimizes particle distribution, thereby improving the estimation accuracy. From the perspective of probability statistics, it proposes a "PauTa Criterion" optimization strategy suitable for various types of particle filter algorithms. This method migrates particles regarded as "impossible" in probability to optimize the particle distribution directly. Simulation experiments show that the "PauTa Criterion" optimization strategy has a good optimization effect on particle filter-based algorithms. The improved methods for both importance density and resampling exhibit excellent estimation performance, which improves the estimation accuracy of the PF and reduces its dependence on the number of particles.