Abstract: In the target tracking method based on traditional particle filter,the importance density function is difficult to select and lack of versatility,and the re-sampling method is difficult to design to solve the particle degradation phenomenon effectively．Therefore,a moving target tracking method based on improved particle filter,using artificial fish swarm algorithm,was proposed to improve the importance density function．Particles interact and coordinate their behavior constantly,making the state of particles close to the posterior distribution,and improve the versatility of the importance density function．On this basis,in order to improve re-sampling method and suppress premature phenomenon,the particle swarm convergence and diversity are balanced by the immune operators of artificial immune algorithm．Experimental results show that compared with traditional particle filter algorithm,moving target tracking accuracy and anti-interfe-rence ability are improved and the particle degradation phenomenon is suppressed effectively by adjusting the parameters of the present algorithm.

Key words: Particle filter,Importance density function,Re-sampling method,Artificial fish swarm algorithm,Artificial immune algorithm,Moving target tracking