Weighted measurement fusion particle filter for nonlinear systems

被引：0
|
作者：
Li Y. [1 ,2 ]
Sun S. [1 ]
Hao G. [1 ]
机构：
[1] School of Electronic Engineering, Heilongjiang University, Harbin
[2] School of Computer and Information Engineering, Harbin University of Commerce, Harbin
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2019年 / 47卷 / 02期
关键词：
Asymptotical optimality; Nonlinear system; Particle filter; Taylor series expansion; Weighted measurement fusion;
D O I：
10.13245/j.hust.190221
中图分类号：
学科分类号：
摘要：
A weighted measurement fusion particle filter for nonlinear multi-sensor systems was proposed. The measurement equations of nonlinear multi-sensor systems were deal with by using Taylor series, so that they had approximate linear relationships. On this basis, a universal weighted measurement fusion particle filter (WMF-PF) was presented by using weighted measurement fusion (WMF) algorithm and the well-known particle filter (PF). The WMF-PF can deal with nonlinear fusion problems with any noise statistics. It can reduce the computational cost and improve the real-time by compressing measurement information from multiple sensors. The proposed WMF-PF approaches to the centralized measurement fusion particle filter (CMF-PF) asymptotically with the increasing of Taylor series expansion, so it has the asymptotical global optimality. Two examples were given to show the effectiveness of the proposed algorithms. © 2019, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.
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页码：115 / 120
页数：5
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