基于信息熵的多维属性自适应关联算法

Multidimensional Attribute Adaptive Association Algorithm Based on Information Entropy

目前战场智能化、信息化水平不断提升,跨域联合作战成为必然趋势。无源侦察作为有源侦察的有力补充,往往采用无源多传感器联合的方式实现对重点区域的监控。为提高关联准确性,无源多传感器目标关联常采用时空及辐射源参数综合度量的方式。其中,基于辐射源参数关联往往采用模板匹配的方式,根据经验预设属性权重,对人工依赖程度较高。提出一种基于信息熵的多维属性自适应关联算法,该算法基于辐射源参数构造多维度距离因子属性,同时采用信息熵实现属性权重的动态分配。最后在复杂场景下的仿真验证表明,该算法在目标参数和位置相近的电磁环境中具有较好的关联效果。

With the continuous improvement of intelligence and information technology in battlefield,joint operation cross-domain is becoming an inevitable trend.As a completely supplement of active reconnaissance,passive reconnaissance is often used to monitor key areas by multi-sensor.In order to improve accuracy of multi-target association,a comprehensive measurement of space-time character and radar emitter parameter is applied,especially the template matching method,which is highly depended on expert knowledge.A adaptive association algorithm for multidimensional attribute based on information entropy is proposed,which is built on multi-distance factor and dynamic weight.A better relevance which targets have similar parameter and position is verified in the complex simulation scene.