Particle Filter Algorithm Based on Statistical Linear Regression
基于统计线性回归的粒子滤波方法

In this paper, a new particle filter based on Statistical Linear Regression (SLR) is proposed for the state estimation of non-Gauss nonlinear systems. In the new algorithm, the importance density function of particle filter is generated by linearizing the nonlinear function using statistical linear regression through a set of Gauss-Hermite quadrature points estimating regression coefficient. The density function integrates the new observations into system state transition and extends the overlap fields with true posterior density. The simulation shows that the new algorithm not only has high estimation accuracy but also has better stability and less computation amount than the PF.