基于信息增益的传感器搜索调度模型对比分析

Comparison of the Information Gain Based Sensor Search Scheduling Models

针对目标搜索传感器调度问题,归纳总结了基于Shannon熵、Kullback-Leibler熵和Rényi熵3种信息测度以及全局和局部两种信息增益,衍生出6种基于信息增益的搜索调度模型.通过数值仿真计算对比分析6种基于信息增益的搜索调度模型的目标捕获性能,结果表明基于3种信息测度的搜索调度模型对目标的捕获性能差别很小,而基于全局信息增益的搜索调度模型性能更优,仿真结果可为工程实践中传感器搜索调度方法的优选提供参考.

To deal with the problem of sensor search scheduling,three typical information measures based on Shannon entropy,Kullback-Leibler entropy and Rényi entropy are summarized. Additionally,based on both of the global information gain and local information gain,6 kinds of search scheduling models are developed. The acquisition performance of all information based scheduling models are compared through simulation. The results indicate that,as for the three typical information measures,the acquisition performance of the search scheduling methods shows the same rules,while search scheduling model based on the global information gain shows better performance. The simulation results can be used as the reference for the search scheduling method in project.