Order-Statistic Based Target Detection with Compressive Measurements in Single-Frequency Multistatic Passive Radar
Order-Statistic Based Target Detection with Compressive Measurements in Single-Frequency Multistatic Passive Radar
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DOI：
10.1016/j.sigpro.2022.108785
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Junhu Ma;Hongbin Li;Lu Gan
中科院分区：
文献类型：
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作者：
Junhu Ma;Hongbin Li;Lu Gan
研究了基于单频网（SFN）的多基地无源雷达系统（MS-PRS）在直达波干扰（DPI）和杂波干扰下的压缩目标检测问题。具体地，测量矩阵被设计为联合获得压缩观测并去除DPI和杂波。我们首先分析了一个压缩子空间检测器，假设目标的支持是已知的。当目标支持度无法准确获取时，提出了一种基于顺序统计量的检测器，简称OSOMP，该检测器利用正交匹配追踪（OMP）算法估计目标支持度，然后将压缩观测值投影到估计子空间中.由于OMP应用迭代排序过程来选择字典的组件/原子，因此OSOMP测试变量是具有非收敛分布的顺序统计量。科普这一问题，提出了一种改进的OSOMP检测器检验统计量，并得到了虚警概率的解析表达式。我们进一步讨论了OSOMP算法所需的最小迭代次数，以实现所需的检测概率和虚警。数值仿真验证了理论分析，并说明了所提出的检测器相对于几个基准检测器的性能。
This paper considers the problem of compressive target detection with direct-path interference (DPI) and clutter in a single-frequency network (SFN) based multistatic passive radar system (MS-PRS). Specifically, a measurement matrix is designed to jointly obtain compressive observations and remove the DPI and clutter. We first analyze a compressive subspace detector which assumes the target support is known. When the target supports cannot be accurately obtained, an order-statistic (OS) based detector, referred to as the OSOMP, is proposed by using the orthogonal matching pursuit (OMP) algorithm to estimate the target support, and then projecting the compressive observations into the estimated subspace. Since OMP applies an iterative ranking process to select the components/atoms of the dictionary, the OSOMP test variable is an order statistic, which has a non-convergent distribution. To cope with this problem, a modified test statistic for the OSOMP detector is presented and an analytical expression for the probability of false alarm is obtained. We further discuss the minimum number of iterations required by the OSOMP algorithm to achieve the desired probability of detection and false alarm. Numerical simulations are conducted to verify the theoretical analysis and illustrate the performance of the proposed detector relative to several benchmark detectors.