摘要翻译：
本文研究了在复合高斯杂波中，当雷达信号不完全已知时的雷达检测问题。提出了一种基于广义似然比检验的高斯复合杂波点状目标检测方法。采用天线阵列，我们假定实际的转向矢量偏离标称的方向矢量，但位于一个已知的区间内。然后通过采用半定规划来确保检测。仿真结果表明，与自适应归一化匹配滤波器相比，该检测器在完全匹配情况下的检测损失可以忽略不计，但在非匹配情况下的检测损失优于自适应归一化匹配滤波器。该检测器对杂波协方差矩阵具有恒虚警率。
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英文标题：
《Robust Radar Detection of a Mismatched Steering Vector Embedded in
  Compound Gaussian Clutter》
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作者：
Mai P. T. Nguyen, I. Song
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最新提交年份：
2017
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  The problem of radar detection in compound Gaussian clutter when a radar signature is not completely known has not been considered yet and is addressed in this paper. We proposed a robust technique to detect, based on the generalized likelihood ratio test, a point-like target embedded in compound Gaussian clutter. Employing an array of antennas, we assume that the actual steering vector departs from the nominal one, but lies in a known interval. The detection is then secured by employing a semi-definite programming. It is confirmed via simulation that the proposed detector experiences a negligible detection loss compared to an adaptive normalized matched filter in a perfectly matched case, but outperforms in cases of mismatched signal. Remarkably, the proposed detector possesses constant false alarm rate with respect to the clutter covariance matrix.
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PDF链接：
https://arxiv.org/pdf/1710.02656