摘要翻译：
基于微多普勒的汽车雷达目标分类能力可以为未来的主动安全汽车提供高可靠性和短延迟的功能。在实际的城市场景中，大量的行人包围着车辆，要求对他们的对待水平进行优先级排序。将穿过街道或在车辆路径内的相关行人与那些在人行道上并沿着车辆路线移动的行人进行分类，可以大大减少车辆对行人的事故数量。本文提出了一种将行人视为复杂分布目标并利用其微多普勒(MD)雷达特征的行人方向估计方法。MD特征表示行人的运动方向，并使用监督回归估计运动方向与相应MD特征之间的映射。为了获得更高的回归性能，本文采用了计算机视觉领域最先进的基于稀疏字典学习的特征提取算法，将多普勒效应与视频时间梯度进行了比较。在一个实际的汽车场景仿真中，用一个带有二维矩形阵列的多输入多输出(MIMO)汽车雷达观察步行行人，评估了该方法的性能。模拟数据采用统计Boulic-Thalman人体运动模型生成。采用支持向量回归(SVR)和基于多层感知器(MLP)的回归算法实现了精确的运动方向估计。结果表明，在距离雷达$100$m范围内，行人的方向估计误差在$95%的试验情况下小于$10^circ}$。
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英文标题：
《Pedestrian Motion Direction Estimation Using Simulated Automotive MIMO
  Radar》
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作者：
Petro Khomchuk, Inna Stainvas, Igal Bilik
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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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一级分类：Computer Science        计算机科学
二级分类：Information Theory        信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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一级分类：Mathematics        数学
二级分类：Information Theory        信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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英文摘要：
  Micro-Doppler-based target classification capabilities of the automotive radars can provide high reliability and short latency to the future active safety automotive features. A large number of pedestrians surrounding vehicle in practical urban scenarios mandate prioritization of their treat level. Classification between relevant pedestrians that cross the street or are within the vehicle path and those that are on the sidewalks and move along the vehicle rout can significantly minimize a number of vehicle-to-pedestrian accidents.   This work proposes a novel technique for a pedestrian direction of motion estimation which treats pedestrians as complex distributed targets and utilizes their micro-Doppler (MD) radar signatures. The MD signatures are shown to be indicative of pedestrian direction of motion, and the supervised regression is used to estimate the mapping between the directions of motion and the corresponding MD signatures. In order to achieve higher regression performance, the state of the art sparse dictionary learning based feature extraction algorithm was adopted from the field of computer vision by drawing a parallel between the Doppler effect and the video temporal gradient.   The performance of the proposed approach is evaluated in a practical automotive scenario simulations, where a walking pedestrian is observed by a multiple-input-multiple-output (MIMO) automotive radar with a 2D rectangular array. The simulated data was generated using the statistical Boulic-Thalman human locomotion model. Accurate direction of motion estimation was achieved by using a support vector regression (SVR) and a multilayer perceptron (MLP) based regression algorithms. The results show that the direction estimation error is less than $10^{\circ}$ in $95\%$ of the tested cases, for pedestrian at the range of $100$m from the radar.
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PDF链接：
https://arxiv.org/pdf/1808.00366