Fusion Particle Filter for Nonlinear Systems Based on Segmental Gauss-Hermite Approximation

被引：0
作者：
Li, Yun [1 ]
机构：
[1] Harbin Univ Commerce, Sch Comp & Informat Engn, Harbin 150001, Peoples R China
来源：
IEEE ACCESS | 2020年 / 8卷
基金：
中国国家自然科学基金;
关键词：
Multisensor; nonlinear system; weighted measurement fusion; segmental Gauss-Hermite approximation; particle filter; UNSCENTED KALMAN FILTER; STATE;
D O I：
10.1109/ACCESS.2020.3018032
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Multisensor fusion estimators play an important role in modern information processing. Weighted measurement fusion (WMF) algorithm has widely been applied to data compression of multisensor linear systems. Due to the complexity and uncertainty of nonlinear systems, the application of WMF algorithms is limited in multisensor nonlinear systems. In this article, an approximate linear relationship is established by using the segmental Gauss-Hermite approximation method for multisensor nonlinear systems. Based on the relationship and weighted least squares (WLS) method, a WMF algorithm is presented to compress the data for multisensor nonlinear systems. By combining the WMF algorithm with Particle Filter (PF), a weighted measurement fusion Particle Filter (WMF-PF) is presented for multisensor nonlinear systems. Compared with the centralized fusion PF, the proposed WMF-PF has a fair accuracy and less computational cost. It has a potential application in navigation, GPS, target tracking, communications, big data and so on. An example is given to show the effectiveness of the proposed algorithms.
引用
收藏
页码：151731 / 151739
页数：9
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