摘要翻译：
多输入多输出(MIMO)雷达因其天线和波形分集而具有传统雷达无法比拟的优越性。尽管MIMO雷达具有更高的角分辨率、更好的参数识别性和更好的目标检测能力，但其硬件成本（由于多发射机和多接收机）和高能耗（多脉冲）限制了MIMO雷达在大规模网络中的应用。一方面，需要较高的角度和速度估计精度，另一方面，需要较少的天线/脉冲数目。为了实现这样的折衷，本文采用角速度估计器的Cram'er-Rao下界(CRLB)作为设计天线和脉冲布局的性能指标。结果表明，由于双目标CRLB同时考虑了模糊函数的主瓣宽度和旁瓣电平，因此与单目标CRLB相比，双目标CRLB是一个更合适的判据。本文提出了几种基于凸和子模优化的天线和脉冲选择算法。数值实验证明了所提出的理论。
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英文标题：
《Sparse Antenna and Pulse Placement for Colocated MIMO Radar》
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作者：
Ehsan Tohidi, Mario Coutino, Sundeep Prabhakar Chepuri, Hamid
  Behroozi, Mohammad Mahdi Nayebi, and Geert Leus
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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：
  Multiple input multiple output (MIMO) radar is known for its superiority over conventional radar due to its antenna and waveform diversity. Although higher angular resolution, improved parameter identifiability, and better target detection are achieved, the hardware costs (due to multiple transmitters and multiple receivers) and high energy consumption (multiple pulses) limit the usage of MIMO radars in large scale networks. On one hand, higher angle and velocity estimation accuracy is required, but on the other hand, a lower number of antennas/pulses is desirable. To achieve such a compromise, in this work, the Cram'er-Rao lower bound (CRLB) for the angle and velocity estimator is employed as a performance metric to design the antenna and pulse placement. It is shown that the CRLB derived for two targets is a more appropriate criterion in comparison with the single-target CRLB since the two-target CRLB takes into account both the mainlobe width and sidelobe level of the ambiguity function. In this paper, several algorithms for antenna and pulse selection based on convex and submodular optimization are proposed. Numerical experiments are provided to illustrate the developed theory.
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PDF链接：
https://arxiv.org/pdf/1805.10641