基于最小代价和蚁群算法的传感器资源优化研究

Research on MultiSensor Resource Optimization Based on MinimumCost and Ant Colony Algorithm

摘要: 战场环境的复杂性要求使用多种传感器对战场目标进行综合敌我识别。为充分发挥综合系统的功能，需对有限的传感器资源进行科学合理分配。本文通过分析综合敌我识别的特点，得出传感器资源优化模型的约束条件，提出利用代价作为传感器资源优化的准则，并分析了各类源代价及其量化方法，从而建立了综合敌我识别传感器资源优化数学模型。同时将蚁群优化算法的思想引入到传感器资源优化中，并进行了算法仿真。实验结果证实了该方法的有效性。

Abstract: Under complex battlefield environment,multisensor integrated identification Friend or Foe (MSIIFF) may be needed.In order to make best use of MSIIFF,the multisensor resources optimization(MSRO) is put forward.Through Analyzing characteristics of MSIIFF,this paper presents the restricted condition.At the same time this paper introduces the cost as the rule of MSRO.Thereby,the maths model of MSRO is constructed.In succession,this paper combines ant colony optimization(ACO) algorithm and MSRO.The results have shown the availability of this method through simulation.