基于未知多普勒频移的距离扩展目标检测 半定规划

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摘要翻译：
基于广义似然比检验技术，研究了高斯噪声中多普勒频移距离扩展目标的检测方法。首先，通过半定规划求解与未知多普勒频率估计相关的最大值，导出了一种检测方案。为了降低检测器的计算复杂度，我们考虑在松弛空间上采用最大化来简化检测器。数值分析和理论分析表明，这两种检测器都具有恒虚警率。在已知多普勒频率下，基于半定规划的检测器的检测性能与传统的检测器基本相同。
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英文标题：
《Range-Spread Targets Detection in Unknown Doppler Shift via
  Semi-Definite Programming》
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作者：
Mai. P. T. Nguyen, I. Song, S. Lee, S. Yoon
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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：
  Based on the technique of generalized likelihood ratio test, we address detection schemes for Doppler-shifted range-spread targets in Gaussian noise. First, a detection scheme is derived by solving the maximization associated with the estimation of unknown Doppler frequency with semi-definite programming. To lower the computational complexity of the detector, we then consider a simplification of the detector by adopting maximization over a relaxed space. Both of the proposed detectors are shown to have constant false alarm rate via numerical or theoretical analysis. The detection performance of the proposed detector based on the semi-definite programming is shown to be almost the same as that of the conventional detector designed for known Doppler frequency.
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PDF链接：
https://arxiv.org/pdf/1710.02928

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关键词：多普勒 Applications Optimization maximization Conventional 降低 shown 计算 研究 检验