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...

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Bibliographic Details
Main Authors: Fu Chun-Ling, Gong De-Long, Jia Peiyan
Format: Conference Proceeding
Language:English
Subjects:
Filtering algorithms
Monte Carlo methods
Multi-sensor Observation
Noise
Nonlinear Estimation
Optimization methods
Particle Filter
Particle filters
Sensors
Weight Optimization
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Summary: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.
ISSN:1934-1768
2161-2927