Abstract
Particle filter (PF) is an effective approach for the state estimation of nonlinear systems. However, PF still faces many problems which influence the accuracy of state estimation. One typical problem is the particle impoverishment. In this study, an adaptive intelligent particle filter (AIPF) is proposed to mitigate the particle impoverishment problem common in PF. We adopt the idea of evolution to solve the loss of the particle diversity based on genetic algorithm (GA) and adaptive genetic algorithm (AGA). The general PF can be regarded as a particular instance of AIPF. Further, the proposed method does not need the process of coding and the calculation of parameter for GA. Finally, the effectiveness of AIPF is verified by two simulation examples.
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