发帖
雷达卡
摘要翻译：
针对多个窄带任意相关源的直接定位问题，提出了一种新的解决方案，即部分校准阵列，即由完全校准的子阵组成的阵列，但没有阵间校准。提出的解决方案在性能和计算复杂度方面各不相同。首先，我们提出了一种松弛的极大似然解，它的集中似然只涉及到源的未知位置，并且需要在每个潜在位置对阵列协方差矩阵进行特征分解。为了减少计算量，我们引入了一种近似，它消除了在每个潜在位置进行这种特征分解的需要。为了进一步减少计算量，提出了新的类MUSIC和类MVDR算法，它们比现有的算法在计算上简单得多。通过仿真对这些解决方案的性能进行了评价和比较。
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英文标题：
《Direct Localization of Multiple Sources by Partly Calibrated Arrays》
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作者：
Amir Adler, Mati Wax
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最新提交年份：
2018
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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：
We present novel solutions to the problem of direct localization of multiple narrow-band and arbitrarily correlated sources by partly calibrated arrays, i.e., arrays composed of fully calibrated sub-arrays yet lacking inter-array calibration. The solutions presented vary in their performance and computational complexity. We present first a relaxed maximum likelihood solution whose concentrated likelihood involves only the unknown locations of the sources and requires an eigen-decomposition of the array covariance matrix at every potential location. To reduce the computational load, we introduce an approximation which eliminates the need for such an eigen-decomposition at every potential location. To further reduce the computational load, novel MUSIC-like and MVDR-like solutions are presented which are computationally much simpler than the existing solutions. The performance of these solutions is evaluated and compared via simulations.
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PDF链接：
https://arxiv.org/pdf/1807.09931
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