Title :
A novel robust MM filter for target tracking with glints
Author :
Yong Liu ; Yan Liang ; Quan Pan
Author_Institution :
Sch. of Autom., Northwestern Polytech. Univ., Xi´an, China
Abstract :
Interacting Multiple Model (IMM) filter faces significant outlier-caused peak-errors. In this paper, the Bayesian probability update in IMM is found equivalent to Dempster´s Rule of Combination which cannot handle evidence conflicts caused by outliers. Furthermore, a novel robust MM (RMM) filter is proposed through introducing expert rules about mode evolvement and presenting the Likelihood Temporal Ratio (LTR) and building the Induced Combination Rule (ICR). Simulations about target tracking show the effectiveness of the proposed method.
Keywords :
Bayes methods; filtering theory; maximum likelihood estimation; target tracking; Bayesian probability update; Dempster rule-of-combination; ICR; IMM filter; LTR; RMM filter; induced combination rule; interacting multiple model filter; likelihood temporal ratio; outlier-caused peak-errors; robust MM filter; target tracking; Bayes methods; Filtering theory; Information filtering; Noise; Robustness; Switches; Target tracking; Dempster-Shafer Theory; Interacting Multiple Model; Non-Gaussian Noise; Outlier; Robust Filter;
Conference_Titel :
Control Conference (CCC), 2013 32nd Chinese
Conference_Location :
Xi´an
