非线性系统中状态和参数联合估计的双重粒子滤波方法

该文提出了一种双重粒子滤波方法，对存在未知参数的非线性系统进行状态和参数联合估计。该方法采用基于充分统计量的粒子滤波技术，避免了重采样过程中的粒子枯竭现象；采用贝塔分布拟合系统参数的后验分布，不仅充分利用了先验信息，而且避免了对高斯分布拖尾部分的采样，提高了粒子的采样效率。仿真实验结果表明，该方法提高了非线性系统中状态和参数的估计精度，降低了滤波器对初始误差的敏感性。

A Dual Particle Filter for State and Parameter Estimation in Nonlinear System

The dual particle filter is proposed to solve the problem of simultaneously estimating the state and the parameter of a nonlinear dynamic system. In the new filter, the sufficient statistics based particle filter is adopted to deal with sampling impoverishment arising in generic particle filter and the beta distribution, which makes good use of the prior knowledge as well as avoids tail draws for the parameter, is used to fit the parametric a posteriori probability density function. Simulation results show that both estimation accuracy and initial sensitivity of the nonlinear system are improved.