A novel particle weight optimization method based on multi-sensor observation fusion
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.
Nonlinear Estimation Multi-sensor Observation Weight Optimization Particle Filter
FU Chun-ling GONG De-long JIA Peiyan
Basic Experiment Teaching Center, Henan University ,Kaifeng,475004, P.R.China College of Computer and Information Engineering, Henan university, Henan University, Kaifeng 475004,
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
762-766
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）