摘要
通过对目标搜索区域不确定性定量描述的信息熵及信息熵变化而产生的信息增量 ,本文提出了一种多传感器对多目标进行检测与分类的优化算法 ,即针对每一检测单元计算该时刻的信息熵及预测下一时刻的信息熵 ,产生信息增量最大的检测单元就是下一次要搜索的单元 .仿真结果证明了该算法的有效性 .
With information gain obtained by information entropy and evolution of information entropy which characterize uncertainty of target search location,an optimizing algorithm of detection and classification used in multisensors and multitargets is put forward.Namely,information entropy of current sampling time is computed and information entropy of next sampling time is predicted for each detection cell.The detection cell of maximum information gain is the next one to search.The effectiveness of algorithm is testified by simulation results.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2000年第9期39-41,22,共4页
Acta Electronica Sinica
基金
国家自然科学基金!(No.69772 0 31 )
河南省自然科学基金!(No .9980 4 0 0 30 )
关键词
信息融合
传感器管理
信息熵
information fusion
sensor management
information entropy
information gain