高斯背景中一类新的自适应阵列检测器

A New Adaptive Array Detector in Gaussian Background

针对现有文献对自适应阵列检测问题的研究相对比较少的问题,构建高斯背景中一类新的自适应阵列检测器。从新的视角理解高斯背景中自适应阵列检测器的原理,分析了信号子空间建模目的,以及结构化与无结构化检测器、解耦与不解耦检测器之间的性能差异,并提出了调节因子的概念,构造并分析了一类新的结构化检测器。仿真结果表明:从计算负担和检测能力2方面考虑,Hyung Soo Kim的解耦结构化Kelly检测器,Yow-Ling Gau的降秩检测器,以及构造的解耦结构化Kalson检测器都具有比其他检测器更好的综合性能。该研究可为寻找非高斯背景中自适应阵列检测器的构造方法提供参考。

According to the researches on the adaptive array detection in the existing literatures are less, this paper designs a novel adaptive array detector in Gaussian background. This paper comprehends the principle of adaptive array detector in Gaussian background with a new point of view, analyses the modeling purposes of signal subspace, and the performance differences between the structured and unstructured detector, the decoupled and non-decoupled detector. This paper presents the concept of adjustment factors, and analyses a new type of detector structure. Simulation results show that considering both the viewpoint of computational burden and the detection capabilities, Hyung Soo Kim Kellly decoupling structured detector, Yow-Ling Gau＇s reduced-rank detector, and the structured decoupling detector have better performance than other detectors. This paper can provide a reference for finding adaptive array detector constructor under the non-Gaussian background.