Target tracking algorithm based on support vector machine particle filter

To solve the problems of particle degeneration in traditional particle filter, an improved target tracking algorithm was proposed based on density estimation with support vector machines. Using support vector machines, the posterior probability density function of the state was estimated with predicted particles and their important weights during filter iteration. After resampling the new particles from this density model, the degeneration of the filter was eliminated effectively by these diversiform particles. Simulation results demonstrate that the proposed algorithm can increase the quantity of effective particles obviously, and the new filter is superior to the Markov Chain Monte Carlo particle filter and regularized filter.