Sensors Decision Fusion Algorithm Based on the Learning Strategy

摘要：

In the target detection of radar and sonar systems,its difficult to give prior probability of the targets appearance and the cost of systems wrong decision. In some practical applications,the probability of targets appearance will continually change. It is difficult for the existing distributed systems decision fusion algorithm to solve the decision fusion problem of unknown and variable targets.In this paper learning strategies is used to estimate target probability in real-time and to achieve adaptive decision fusion.Analysis shows that,in the detection of unknown and variable targets, this algorithm can adaptively modify related parameters according to the detected objects.The detection performance has good convergence with the increase of study time and the algorithm performance is better than NP and Bayes algorithm.

关键词： distributed sensor the bayesian theory learning strategy.

作者: Hu Xuehai Wang Houjun Ren Dairong

作者单位: Automation Engineering School,University of Electronic Science and Technology of China,Chengdu Quality Department,ChengDu Aircraft Design & Research Institute (CADI)Chengdu,Xiyuan Way 2006,China

会议类型: 国际会议

会议名称: 2011 10th International Conference on Electronic Measurement & Instruments(第十届电子测量与仪器国际会议 ICEMI2011)

会议地点: 成都

会议语种:英文

页码: 168-171

在线出版日期: 2011-08-16（万方平台首次上网日期，不代表论文的发表时间）