| 摘要: |
| 为了解决训练样本不足时的子空间信号检测问题，提出了两种有效的降秩检测器。基于主分量分析（PCA）的思想，先把常规自适应子空间检测器中采样协方差矩阵（SCM）的求逆运算用噪声特征子空间矩阵与其共轭转置的乘积代替，构造降秩子空间检测器；为进一步提高算法稳健性，把降秩子空间检测器的求逆运算用Moore-Penrose逆代替。仿真结果表明,所提方法在训练样本充足及不足时，均比现有方法具有更好的检测性能。 |
| 关键词: 多通道信号检测；子空间信号检测；自适应信号检测 训练样本不足；降秩方法 |
| DOI：10.3969/j.issn.1001-893x.2017.09.012 |
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| 基金项目:国家自然科学基金资助项目（61501505） |
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| Subspace signal detection with limited training data |
| YANG Xing1,WANG Licai2,YANG Yang3,WANG Helei2,LIU Weijian2 |
| (1.Unit 94402 of PLA,Jinan 250022，China;2.Huangpi NCO School,Air Force Early Warning Academy,Wuhan 430019，China;3.Military Representative Office Stationed at 720 Factory,Nanjing 210046，China)) |
| Abstract: |
| In order to overcome the difficulty of detecting a subspace signal with insufficient training data,two effective reduced-rank subspace detectors are proposed. According to the theory of principal component analysis(PCA),the sample covariance matrix(SCM),contained in conventional detection statistic,is replaced by the production of the noise eign-subspace and its conjugate transpose. This results in reduced-rank subspace detectors. To further improve the robustness,the matrix inversion operation is substituted by the Moore-Penrose inversion. The comparison with conventional detectors shows that the proposed reduced-rank subspace detectors can provide improved detection performance,no matter the number of the training data is sufficient or not. |
| Key words: multichannel signal detection subspace signal detection adaptive signal detection limited training data rank reduction |
