针对标准的粒子滤波存在粒子贫化问题，提出了一种鲸群优化的粒子滤波算法。用粒子表征鲸鱼个体, 模拟鲸鱼群体搜寻猎物的过程，引导粒子向高似然区域移动。将粒子滤波中粒子的状态值作为鲸鱼群的个体位置，将粒子的状态估计转化为对鲸鱼群的寻优；通过鲸群的螺旋运动方式优化粒子的重要性采样过程，使粒子分布更加合理，对鲸群算法中的全局最优值引入最优邻域随机扰动策略，并在鲸鱼位置更新过程中加入自适应权重因子；选用一种典型的单静态非增长模型进行仿真测试。测试结果表明：提出的方法与传统的粒子滤波以及引力场优化的粒子滤波相比，在保证相同粒子数的前提下，算法的均方误差分别降低了28%和9%，证明了鲸群优化的粒子滤波算法具有更高的估计精度，并且在粒子数较少的情况下，可实现更准确的状态估计。

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.