Xiong Gaoqing, Cao Hui, Liu Weijian, Zhang Jialiang, Wang Kehao, Yan Kai
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.
Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, Wuhan University of Technology, Wuhan 430070, China.
Sensors (Basel). 2025 Jan 25;25(3):736. doi: 10.3390/s25030736.
We investigate the problem of detecting the distributed targets buried in the Gaussian noise whose covariance matrix is unknown when signal mismatch occurs. The idea is to add a fictitious signal under the null hypothesis of the origin detection problem so that when signal mismatch occurs, the fictitious signal captures the mismatched signals, thus making the null hypothesis more plausible. More precisely, the fictitious signal is modeled as a Gaussian component with a covariance matrix of a stochastic factor multiplied by a rank-one matrix. The generalized likelihood ratio test (GLRT) is employed to address the modification detection problem. We present an exhaustive derivation of the detector and prove that it possesses the constant false alarm rate (CFAR) property. The performance analysis demonstrates the effectiveness of the proposed detector. When the SNR is 23 dB, as generalized cosine squared decreases from 1 to 0.83, the detection probability of the proposed GLRT-SL drops to 0.65, exhibiting the fastest decline compared to the G-ABORT-HE, which falls to 0.98, and the GW-ABORT-HE, which decreases to 0.85.
我们研究在信号失配发生时，检测埋于协方差矩阵未知的高斯噪声中的分布式目标这一问题。其思路是在原点检测问题的零假设下添加一个虚拟信号，以便在信号失配发生时，虚拟信号能捕获失配信号，从而使零假设更合理。更确切地说，虚拟信号被建模为一个高斯分量，其协方差矩阵为一个随机因子乘以一个秩一矩阵。采用广义似然比检验（GLRT）来处理修正检测问题。我们给出了该检测器的详尽推导，并证明它具有恒虚警率（CFAR）特性。性能分析证明了所提检测器的有效性。当信噪比为23 dB时，随着广义余弦平方从1降至0.83，所提GLRT - SL的检测概率降至0.65，与降至0.98的G - ABORT - HE和降至0.85的GW - ABORT - HE相比，下降速度最快。
