Title :
A novel particle weight optimization method based on multi-sensor observation fusion
Author :
Chun-ling, Fu ; De-long, Gong ; Peiyan, Jia
Author_Institution :
Basic Exp. Teaching Center, Henan Univ., Kaifeng, China
Abstract :
Aiming at the effective realization of particle filter in multi-sensor observation system, a novel particle weight optimization method based on multi-sensor observation fusion is proposed in this paper. In the new algorithm, the observation likelihood function is firstly constructed on the basis of the concrete form of proposal distribution, and all observations at current sampling time are used to calculate particle weight, respectively. Next, on the basic assumption of sensors with identical accuracy, combined with average weighted strategy, the weighting fusion method is used to further optimize every particle weight in multi-sensor observation. Finally, the filter precision is improved by decreasing the variance of particle weights. The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.
Keywords :
optimisation; particle filtering (numerical methods); sensor fusion; signal sampling; statistical distributions; average weighted strategy; filter precision improvement; multisensor observation fusion; multisensor observation system; observation likelihood function; particle filter; particle weight optimization method; proposal distribution; sampling time; weighting fusion method; Filtering algorithms; Monte Carlo methods; Noise; Optimization methods; Particle filters; Sensors; Multi-sensor Observation; Nonlinear Estimation; Particle Filter; Weight Optimization;
Conference_Titel :
Control Conference (CCC), 2012 31st Chinese
Conference_Location :
Hefei
Print_ISBN :
978-1-4673-2581-3
