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Indexed by:会议论文

Date of Publication:2006-06-21

Included Journals:EI、CPCI-S、Scopus

Volume:2

Page Number:5350-+

Key Words:optimized grouping; weighted fusion; maximum likelihood principle; partheno-genetic algorithm; optimal estimation

Abstract:When measuring a certain state, multi-sensor can be divided, into several groups, then processed by grouping weighted fusion algorithm. Based on the measurement equation of the state :and the model of the noise, optimal weights of grouping fusion algorithm can be obtained by the principle of maximum likelihood estimation, and optimal grouping way of multi-sensor can be constructed by parthenogenetic algorithm. According to the methods mentioned above, a weighted, fusion algorithm of multi-sensor based on optimized grouping is presented in the paper, which can achieve the optimal estimation of the state to be measured.