摘要翻译：
人的计数是传感应用中最热门的问题之一。脉冲无线电超宽带（IR-UWB）雷达已广泛应用于人员计数，提供了一种无照明和隐私问题的无设备解决方案。然而，由于信号的叠加和阻塞，现有的解决方案在拥塞环境中的性能受到限制。本文提出了一种基于曲率变换和距离宾的混合特征提取方法。利用曲率变换在多尺度、多角度提取二维雷达矩阵特征。此外，通过将矩阵的每一行沿传播距离划分为若干个区间来选择特征，从而引入了距离区间。构建了三种密集场景下的雷达信号数据集，包括人群在每平方米3人和4人的约束区随机行走，平均距离为10厘米的排队。数据集中的人数最多为20人。通过对决策树、AdaBoost、随机森林和神经网络四种分类器的比较，验证了混合特征的有效性，其中随机森林在三种密集场景下的分类准确率最高，达到97%以上。此外，为了保证混合特征的可靠性，还对聚类特征、活动特征和CNN特征进行了比较。实验结果表明，本文提出的混合特征提取方法性能稳定，效果显著。
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英文标题：
《Dense People Counting Using IR-UWB Radar with a Hybrid Feature
  Extraction Method》
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作者：
Xiuzhu Yang, Wenfeng Yin, Lei Li and Lin Zhang
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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：
  People counting is one of the hottest issues in sensing applications. The impulse radio ultra-wideband (IR-UWB) radar has been extensively applied to count people, providing a device-free solution without illumination and privacy concerns. However, performance of current solutions is limited in congested environments due to the superposition and obstruction of signals. In this letter, a hybrid feature extraction method based on curvelet transform and distance bin is proposed. 2-D radar matrix features are extracted in multiple scales and multiple angles by applying the curvelet transform. Furthermore, the distance bin is introduced by dividing each row of the matrix into several bins along the propagating distance to select features. The radar signal dataset in three dense scenarios is constructed, including people randomly walking in the constrained area with densities of 3 and 4 persons per square meter, and queueing with an average distance of 10 centimeters. The number of people is up to 20 in the dataset. Four classifiers including decision tree, AdaBoost, random forest and neural network are compared to validate the hybrid features, and random forest performs the highest accuracies of all above 97% in three dense scenarios. Moreover, to ensure the reliability of the hybrid features, three other features including cluster features, activity features and CNN features are compared. The experimental results reveal that the proposed hybrid feature extraction method exhibits stable performance with significantly superior effectiveness.
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PDF链接：
https://arxiv.org/pdf/1806.06629