一种用于解决非线性滤波问题的新型粒子滤波算法

粒子滤波算法受到许多领域的研究人员的重视,该算法的主要思想是使用一个带有权值的粒子集合来表示系统的后验概率密度.在扩展卡尔曼滤波和Unscented卡尔曼滤波算法的基础上,该文提出一种新型粒子滤波算法.首先用Unscented卡尔曼滤波器产生系统的状态估计,然后用扩展卡尔曼滤波器重复这一过程并产生系统在k时刻的最终状态估计.在实验中,针对非线性程度不同的两种系统,分别采用5种粒子滤波算法进行实验.结果证明,文中所提出的算法的各方面性能都明显优于其他4种粒子滤波算法.

A New Particle Filter for Nonlinear Filtering Problems

Particle filters have gained special attention of researchers in various fields. The key idea of this technique is to represent the posterior density by sets of weighed samples. This paper proposes a new particle filter which is based on the extended Kalman filter and the Unscented Kalman filter. It first uses the former to generate an estimate of the state at time k, and then uses the latter to repeat the process and to gain the final estimate of the state and corresponding covariance at time k. In the experiments, the authors test five different particle filters on two different nonlinear systems. The experimental results indicate that the proposed particle filter has much better performance than the other four particle filters do.