A weighted measurement fusion particle filter for nonlinear multisensory systems based on Gauss-Hermite approximation

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.