Traditional Particle Filter (PF) algorithm based on Particle Swarm Optimization (PSOPF), which moves the moving particles to the high likelihood region, destroys the prediction distribution. When the likelihood function has many peaks, it has a large computation amount while filtering performance does not improved significantly. To solve this problem, a new PSOPF based on the Adjustment of the Likelihood (LA-PSOPF) was proposed. Under the premise of preserving the prediction distribution, the Particle Swarm Optimization (PSO) algorithm was used to adjust the likelihood distribution to increase the number of effective particles and improve the filtering performance. Meanwhile, a strategy of local optimization was introduced to scale down the swarm of PSO, reduce the amount of calculation and achieve the balance of accuracy and speed of estimation. The simulation results show that the proposed algorithm is better than PF and PSOPF when the measurement error is small and the likelihood function has many peaks, and the computing time is less than that of PSOPF.