文章研究了背景为子空间干扰加高斯杂波的距离扩展目标方向检测问题。杂波是均值为零协方差矩阵未知但具有斜对称特性的高斯杂波,目标与干扰分别通过具备斜对称特性的目标子空间和干扰子空间描述。针对方向检测问题,利用上述斜对称性,根据广义似然比检验(Generalized Likeli-hood Ratio Test,GLRT)准则的一步与两步设计方法,设计了基于GLRT的一步法与两步法的距离扩展目标方向检测器。通过理论推导证明了这2种检测器相对于未知杂波协方差矩阵都具有恒虚警率。对比相同背景下已有检测器,特别是在辅助数据有限的场景下,文章提出的2个检测器表现出了优越的检测性能。
利用正确的先验知识,比如先验杂波谱,往往能够提高检测性能。讨论了在已知和未知先验杂波谱下的机载距离扩展目标检测问题。已知先验杂波谱情况下,提出了基于知识辅助的广义似然比检验(Generalized Likelihood Ratio Test,GLRT)检测器。在先验杂波谱未知的情况下,提出了基于压缩感知(Compressive Sensing,CS)的GLRT检测器。将提出的检测器和传统的检测器进行对比。仿真结果表明,所提出的检测器相比于传统的检测器在性能上有所提高。
Spectrum sensing is an essential ability to detect spectral holes in cognitive radio( CR) networks. The critical challenge to spectrum sensing in the wideband frequency range is how to sense quickly and accurately. Compressive sensing( CS) theory can be employed to detect signals from a small set of non-adaptive,linear measurements without fully recovering the signal. However,the existing compressive detectors can only detect some known deterministic signals and it is not suitable for the time-varying amplitude signal,such as spectrum sensing signals in CR networks. First,a model of signal detect is proposed by utilizing compressive sampling without signal recovery,and then the generalized likelihood ratio test( GLRT) detection algorithm of the time-varying amplitude signal is derived in detail. Finally, the theoretical detection performance bound and the computation complexity are analyzed. The comparison between the theory and simulation results of signal detection performance over Rayleigh and Rician channel demonstrates the validity of the performance bound. Compared with the reconstructed spectrum sensing detection algorithm,the proposed algorithm greatly reduces the data volume and algorithm complexity for the signal with random amplitudes.
