Particle Filters Based on the Nonlinear Mixed Effect State Space Models

Particle filter algorithm is a new algorithm for solving nonlinear system problems. Typically the particle filter uses importance resampling algorithm,which selects a priori distribution. But it is easily affected by external observation,leading to larger changes in weights. This paper introduces an auxiliary particle filter algorithm to improve this. The advantage of this algorithm is that the sample at the previous time is based on the current observational data,thus the sample obtained being closer to the true state. Simulation shows that the auxiliary particle filter algorithm is more effective than the sampling importance resampling.