The problem of adaptive radar detection in compound-Gaussian clutter without secondary data is considered in this paper.In most practical applications,the number of training data is limited.To overcome the lack of tra...The problem of adaptive radar detection in compound-Gaussian clutter without secondary data is considered in this paper.In most practical applications,the number of training data is limited.To overcome the lack of training data,an autoregressive(AR)-process-based covariance matrix estimator is proposed.Then,with the estimated covariance matrix the one-step generalized likelihood ratio test(GLRT) detector is designed without training data.Finally,detection performance of our proposed detector is assessed.展开更多
广义似然比检测(Generalized Likelihood Ratio Test,GLRT)是解决复合高斯杂波下扩展目标检测问题的一种有效方法,而当目标速度未知时,经典的GLRT失效。该文针对目标速度未知的情形,提出了一种基于广义特征值分解的扩展目标多普勒频率...广义似然比检测(Generalized Likelihood Ratio Test,GLRT)是解决复合高斯杂波下扩展目标检测问题的一种有效方法,而当目标速度未知时,经典的GLRT失效。该文针对目标速度未知的情形,提出了一种基于广义特征值分解的扩展目标多普勒频率估计算法,可有效估计多普勒频率,并以此为基础设计了一种R-GLRT(Robust GLRT)检测器。仿真结果表明了这种检测器的有效性。展开更多
该文在复合高斯海杂波背景下,以逆Gamma分布作为纹理分量的先验分布模型,研究了1阶高斯(First Order Gaussian,FOG)和2阶高斯(Second Order Gaussian,SOG)两类子空间目标的自适应检测问题。采用两步广义似然比(Generalized Likelihood R...该文在复合高斯海杂波背景下,以逆Gamma分布作为纹理分量的先验分布模型,研究了1阶高斯(First Order Gaussian,FOG)和2阶高斯(Second Order Gaussian,SOG)两类子空间目标的自适应检测问题。采用两步广义似然比(Generalized Likelihood Ratio Test,GLRT)推导了检测统计量,并分别采用采样协方差矩阵(Sample Covariance Matrix,SCM)、归一化采样协方差矩阵(Normalized Sample Covariance Matrix,NSCM)和定点估计(Function Point Estimation,FPE)作为协方差矩阵估计值,与GLRT相结合,构造出新的自适应检测器。由于该文检测器在设计阶段考虑了海杂波的先验分布模型,且在检测阶段采用了与工作环境相匹配的模型参数,经性能分析与验证,其在检测性能上优于已有匹配滤波(Adaptive Matched Filter,AMF)和归一化匹配滤波(Adaptive Normalized Matched Filter,ANMF)检测器。展开更多
基金supported by the Fundamental Research Funds for the Central Universities under Grant No. E022050205
文摘The problem of adaptive radar detection in compound-Gaussian clutter without secondary data is considered in this paper.In most practical applications,the number of training data is limited.To overcome the lack of training data,an autoregressive(AR)-process-based covariance matrix estimator is proposed.Then,with the estimated covariance matrix the one-step generalized likelihood ratio test(GLRT) detector is designed without training data.Finally,detection performance of our proposed detector is assessed.
文摘广义似然比检测(Generalized Likelihood Ratio Test,GLRT)是解决复合高斯杂波下扩展目标检测问题的一种有效方法,而当目标速度未知时,经典的GLRT失效。该文针对目标速度未知的情形,提出了一种基于广义特征值分解的扩展目标多普勒频率估计算法,可有效估计多普勒频率,并以此为基础设计了一种R-GLRT(Robust GLRT)检测器。仿真结果表明了这种检测器的有效性。
文摘该文在复合高斯海杂波背景下,以逆Gamma分布作为纹理分量的先验分布模型,研究了1阶高斯(First Order Gaussian,FOG)和2阶高斯(Second Order Gaussian,SOG)两类子空间目标的自适应检测问题。采用两步广义似然比(Generalized Likelihood Ratio Test,GLRT)推导了检测统计量,并分别采用采样协方差矩阵(Sample Covariance Matrix,SCM)、归一化采样协方差矩阵(Normalized Sample Covariance Matrix,NSCM)和定点估计(Function Point Estimation,FPE)作为协方差矩阵估计值,与GLRT相结合,构造出新的自适应检测器。由于该文检测器在设计阶段考虑了海杂波的先验分布模型,且在检测阶段采用了与工作环境相匹配的模型参数,经性能分析与验证,其在检测性能上优于已有匹配滤波(Adaptive Matched Filter,AMF)和归一化匹配滤波(Adaptive Normalized Matched Filter,ANMF)检测器。