CAJ | 학술논문

针对目前红外云杂波下点目标检测性能评价方法存在的局限性，提出了一种新的评价方法。首先，对影响检测性能的各要素，建立了杂波量化模型、探测器噪声模型和目标能量量化及传递模型；结合检测器理论，构造了算法性能表征参数。然后，将量化结果作为输入，利用基于遗传算法的BP 神经网络建立量化结果与算法性能表征参数的数值关系。最后以Top-Hat算法和Butterworth滤波器为例，采用该方法评价并分析了其目标检测性能。实验表明，本方法误差在5×10－4以下，具有较高的评估精度。本方法将影响检测性能的耦合因素进行解耦合，通过该模型，不仅可以掌握算法性能变化规律，还能够为探测系统的总体设计和算法的选择提供依据，具有理论意义和工程应用价值。
침대목전홍외운잡파하점목표검측성능평개방법존재적국한성，제출료일충신적평개방법。수선，대영향검측성능적각요소，건립료잡파양화모형、탐측기조성모형화목표능량양화급전체모형；결합검측기이론，구조료산법성능표정삼수。연후，장양화결과작위수입，이용기우유전산법적BP 신경망락건립양화결과여산법성능표정삼수적수치관계。최후이Top-Hat산법화Butterworth려파기위례，채용해방법평개병분석료기목표검측성능。실험표명，본방법오차재5×10－4이하，구유교고적평고정도。본방법장영향검측성능적우합인소진행해우합，통과해모형，불부가이장악산법성능변화규률，환능구위탐측계통적총체설계화산법적선택제공의거，구유이론의의화공정응용개치。
Aiming at the limitations of current performance evaluation methods for point target detection under infra-red cloud clutter, a new evaluation method is proposed.Firstly, for the elements that affect detection performance, the quantitative model of clutter, detector noise model, and quantitative and transfer model of target energy are con-structed;in combination with the detector theory, the algorithm's performance characterization parameter is built;then , using quantified results of the above models as inputs, the mathematical relationship between algorithm's per-formance characterization parameter and quantification results is developed using back-propagation ( BP ) neural network on the basis of genetic algorithm;finally, taking Top-Hat algorithm and Butterworth filter as an example, the method is applied to evaluate and analyze its target detection performance.Experimental results show that the error of the method is below 5×10-4 , which shows high assessment accuracy.The proposed method uncouples the coupling factors influencing the detection performance for the first time.The proposed model is helpful for grasping the change law of the algorithm performance.The proposed model also provides the foundation for the general design of detection system and the selection of algorithms.It has theoretical significance and engineering application value.