The particle filter (PF) has excellent estimation performance for nonlinear non-Gaussian systems. However, this method misleads the results due to sample impoverishment and the sensitivity on the initial values of state and process noise variance. To overcome this, an intelligent PF method with a resampling of multi-population cooperation is presented in this paper. Firstly, an intelligent resampling mechanism based on a circular collaborative structure is proposed. In this mechanism, the particles are divided into multiple populations, which are generated by importance densities with different initial state values and variances individually. Secondly, a collaborative strategy based on Gaussian mutation is designed to improve particle diversity so as to raise estimation accuracy of PF. This strategy reserves the high-weight particles in each population, and replaces the low-weight ones with the particles generated by Gaussian mutation on high-weight particles in the previous population based on the circular collaborative structure. Finally, three systems are introduced to test the performance of the proposed method. The results illustrate that the proposed method can effectively improve the estimation accuracy and robustness of PF when the initial values of state and variance of process noise are unknown compared with three existing methods.

对于非线性非高斯系统，粒子滤波器（PF）具有出色的估计性能。但是，这种方法由于样品的贫乏以及对状态和过程噪声方差初始值的敏感性而误导了结果。为了克服这个问题，本文提出了一种具有多种群合作重采样的智能PF方法。首先，提出了一种基于循环协作结构的智能重采样机制。在这种机制下，粒子被分为多个总体，这些总体是由具有不同初始状态值和方差的重要性密度分别生成的。其次，设计了一种基于高斯变异的协同策略，以提高粒子的多样性，从而提高PF的估计精度。该策略在每个种群中保留了高权重粒子，并基于循环协作结构，将先前种群中高权粒子上的高斯突变产生的粒子替换为低权重粒子。最后，引入三个系统来测试所提出方法的性能。结果表明，与现有的三种方法相比，该方法在未知状态噪声和过程噪声方差的初始值时，可以有效地提高PF的估计精度和鲁棒性。