Aiming at the problem of particle impoverishment in the standard particle filter, a particle filter algorithm based on the whale swarm optimization was proposed. In the algorithm, the particles were used to characterize the individual whales so as to simulate the process of whale swarm for searching preys and guide the particles to move to the high-likelihood region. Firstly, the state value of particles in particle filter was taken as the individual position of the whale swarm, and the state estimation of particles was transformed into the optimization of the whale swarm. Secondly, the importance sampling process of particles was optimized through the spiral motion mode of the whale swarm, which made the particle distribution more reasonable. In addition, the optimal neighborhood random disturbance strategy was introduced for the global optimal value in the whale swarm algorithm, and the adaptive weight factor was added in the process of whale position update. Finally, a typical single-static non-growth model was selected for the simulation test. The test results show that compared with the standard particle filter and the particle filter optimized by the gravitational field, the mean square error of the proposed algorithm is reduced by 28% and 9% respectively under the premise of the same particle number, which verifies that the particle filter algorithm optimized by the whale swarm has the higher estimation accuracy, and in the case of fewer particles, the more accurate state estimation can be achieved.