Introduction - If you have any usage issues, please Google them yourself
Because in non-linear non-Gaussian environment the performance of traditional Kalman Filter in tracking of moving targets is very poor, the paper uses particle filter to track the moving target. Particle filter does not involve linearization around current estimates but rather represent the desired distributions by discrete random measures, which are composed of weighted particles. It has a high accuracy and a rapid convergence. The theory of target tracking based on particle filter is to use these weighted particles to estimate the states of targets. The simulation results of the target model show that in the non-linear non-Gaussian environment, the performance of the particle filter is better than extended Kalman Filter. Finally, we use the particle filter in video tracking, the experimental results further show that in the non-linear non-Gaussian environment the particle filter has a better tracking performance. Particle filter technology can be widely used for air to air, air-groun