A Weighted Measurement Fusion Particle Filter for Nonlinear Multisensory Systems Based on Gauss-Hermite Approximation

Yun Li; Shu Li Sun; Gang Hao

doi:10.3390/s17102222

A Weighted Measurement Fusion Particle Filter for Nonlinear Multisensory Systems Based on Gauss-Hermite Approximation

Sensors (Basel). 2017 Sep 28;17(10):2222. doi: 10.3390/s17102222.

Authors

Yun Li^{1

2}, Shu Li Sun³, Gang Hao⁴

Affiliations

¹ School of Electronic Engineering, Heilongjiang University, Harbin 150080, China. liyunhd@sina.com.
² School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150001, China. liyunhd@sina.com.
³ School of Electronic Engineering, Heilongjiang University, Harbin 150080, China. sunsl@hlju.edu.cn.
⁴ School of Electronic Engineering, Heilongjiang University, Harbin 150080, China. haogang@hlju.edu.cn.

Abstract

We addressed the fusion estimation problem for nonlinear multisensory systems. Based on the Gauss-Hermite approximation and weighted least square criterion, an augmented high-dimension measurement from all sensors was compressed into a lower dimension. By combining the low-dimension measurement function with the particle filter (PF), a weighted measurement fusion PF (WMF-PF) is presented. The accuracy of WMF-PF appears good and has a lower computational cost when compared to centralized fusion PF (CF-PF). An example is given to show the effectiveness of the proposed algorithms.

Keywords: Gauss–Hermite approximation; nonlinear system; particle filter; weighted measurement fusion.