Square-Root Recursive Update Gaussian Particle Filter

被引：0
|
作者：
Liang Z.-B. [1 ]
Liu F.-X. [1 ]
Zhao H.-Z. [1 ]
机构：
[1] Air and Missile Defense College, Air Force Engineering University, Xi'an
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2019年 / 48卷 / 03期
关键词：
Gaussian particle filter; Importance density function; Nonlinear measurements; Recursive update; Square-root;
D O I：
10.3969/j.issn.1001-0548.2019.03.006
中图分类号：
学科分类号：
摘要：
For the construction of importance density function (IDF) of Gaussian particle filter, recursive update Gaussian filter (RUGF) which can effectively overcome the limitation of linear minimum mean square error criterion, updates the target state incrementally based on the gradient of nonlinear measurement function. Consequently, the posterior state estimation that is closer to the real distribution is obtained, but non-positive definite state covariance matrix will lead to recursive interruption. To solve this problem, the square-root implementation strategy of RUGF is firstly analyzed and then square-root recursive update Gaussian filter (SR-RUGF) is implemented by using cubature Kalman filter. Based on that, SR-RUGF is used to construct IDF for Gaussian particle filter. Simulation results demonstrate that the proposed algorithm can effectively solve the recursive interruption problem and obtain estimation result with higher accuracy. © 2019, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.
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页码：345 / 350and373
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