摘要
针对SIRV建模的非高斯杂波背景中距离扩展目标检测问题,首先,假设杂波协方差矩阵结构已知,利用点目标检测器NMF对每个距离单元多个脉冲回波进行相干积累,再对目标所占距离单元能量进行非相干积累,提出了距离扩展目标的积累检测器NMFI,并推导了NMFI虚警概率与检测门限的关系表达式.其次,利用不含目标的辅助单元数据提出了修正的协方差矩阵迭代估计器MRE,再将估计矩阵代替NMFI中的真实协方差矩阵得到自适应检测器MRE-ANMFI,并证明了MRE-ANMFI对杂波纹理分量和协方差矩阵结构的CFAR特性.最后,利用仿真实验验证了本文方法的有效性.
This paper addresses the problem of range-spread target detection in non-Gaussian clutter, which can be modeled as a spherically invariant random vector. First, the covariance matrix structure is assumed to be known, and a normalized matched filter integrator （NMFI） is proposed. The formula relating the false alarm probability to the detection threshold of the NMFI is then deduced. The NMFI statistic is the sum of the NMF statistics, and thus the NMFI outperforms the NMF for range-spread target detection. Moreover, it is assumed that the power spectral density of the baseband equivalent of the clutter is symmetric about f -- 0, and the modified recursive estimator （MRE） of the clutter covariance matrix structure is proposed. The MRE makes full use of the prior clutter information. As the initialization estimation matrix and the recursive progression require only real number operations, the MRE has a lower computational complexity than the adaptive estimator with recursive estimation （AE-RE）. Finally, an adaptive NMFI （ANMFI） with MRE （MRE-ANMFI） is obtained by substitutin~ the estimated matrix for the known covariance matrix structure of the NMFI. The constant false alarm rate properties of the MRE-ANMFI are theoretically proved. Simulation results show that the MRE has higher estimation accuracy than the AE-RE, and the performance of the MRE-ANMFI is better than that of the ANMFI with AE-RE.
出处
《中国科学：信息科学》
CSCD
2013年第4期488-501,共14页
Scientia Sinica(Informationis)
基金
国家自然科学基金(批准号:61032001,61102116)
新世纪优秀人才支持计划(批准号:NCET-11-0872)资助项目
关键词
信号检测
匹配滤波器
雷达杂波
协方差矩阵
最大似然估计
signal detection, matched filter
radar clutter, covariance matrix, maximum likelihood estimation
