Based on the analysis of particle filter algorithm, two improved mechanism are studied so as to improve the performance of particle filter. Firstly, hybrid proposal distribution with annealing parameter is studied in order to use current information of the latest observed measurement to optimize particle filter. Then, resampling step in particle filter is improved by two methods which are based on partial stratified resampling (PSR). One is that it uses the optimal idea to improve the weights after implementing PSR, and the other is that it uses the optimal idea to improve the weights before implementing PSR and uses adaptive mutation operation for all particles so as to assure the diversity of particle sets after PSR. At last, the simulations based on single object tracking are implemented, and the performances of the improved mechanism for particle filter are estimated.
With the inherent deficiency analysis of particle filter algorithm, proposal distribution with adaptive choice
mechanism is studied. The adaptive mechanisms for proposal distribution include adaptive proposal distribution revised
by the information derived from step-by-step Monte Carlo samples, Gaussian approximation adaptive proposal
distribution, shrinking / growing adaptive proposal distribution, adaptive proposal distribution combined with other
methods. At last, the simulations based on single object tracking are implemented, and the performance of the particle
filter with adaptive proposal distribution is verified.