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标题:A particle filter for track-before-detect
时间:2020-01-15 13:01:14
DOI:10.1109/ACC.2001.946220
作者:D.J. Salmond;H. Birch
关键词:Bayes methods;filtering theory;observers;probability;target tracking;Bayesian particle filter;approximation;observers;probability;state estimation
出版源: American Control Conference ,2001
摘要:A Bayesian track-before-detect particle filter is proposed. The filter provides a sample based approximation to the distribution of the target state directly from pixel array data. The filter also provides a measure of the probability that a target is present
大小:514 kb
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