Particle Filter based on Particle Swarm Optimization algorithm (PSO-PF) has high calculation complexity and is easily trapped in local optimum.To solve these problems a novel particle filter based on chaos particle swarm was proposed.On the basis of PSO this algorithm introduced chaos sequence by use of the ergodicity and randomness of chaos the quality of the original sample was improved.In the meanwhile chaos perturbation was utilized to avoid the search being trapped in local optimum.Therefore the algorithm had faster convergence speed and better global search capability.UNGM model was used for simulation to compare the algorithm with Particle Filter (PF) and PSO-PF and Bearings-only tracking (BOT)model was used to verify the effectiveness of the algorithm.The simulation results show that this algorithm not only reduces the local optimization but also improves the velocity and precision so it has a high application value.