SVD-TLS extending Prony algorithm for extracting UWB radar target feature

A new method, SVD-TLS extending Prony algorithm, is introduced for extracting UWB radar target features. The method is a modified classical Prony method based on singular value decomposition and total least squares that can improve robust for spectrum estimation. Simulation results show that poles and residuum of target echo can be extracted effectively using this method, and at the same time, random noises can be restrained to some degree. It is applicable for target feature extraction such as UWB radar or other high resolution range radars.

Dataset of human motion status using IR-UWB through-wall radar

Ultra-wideband(UWB)through-wall radar has a wide range of applications in non-contact human information detection and monitoring.With the integration of machine learning technology,its potential prospects include the physiological monitoring of patients in the hospital environment and the daily monitoring at home.Although many target detection methods of UWB through-wall radar based on machine learning have been proposed,there is a lack of an opensource dataset to evaluate the performance of the algorithm.This published dataset is measured by impulse radio UWB(IR-UWB)through-wall radar system.Three test subjects are measured in different environments and several defined motion status.Using the presented dataset,we propose a human-motion-status recognition method using a convolutional neural network(CNN),and the detailed dataset partition method and the recognition process flow are given.On the well-trained network,the recognition accuracy of testing data for three kinds of motion status is higher than 99.7%.The dataset presented in this paper considers a simple environment.Therefore,we call on all organizations in the UWB radar field to cooperate to build opensource datasets to further promote the development of UWB through-wall radar.

Human Respiration Rate Estimation Using Ultra-wideband Distributed Cognitive Radar System

It has been shown that remote monitoring of pulmonary activity can be achieved using ultra-wideband (UWB) systems, which shows promise in home healthcare,rescue,and security applications.In this paper,we first present a multi-ray propagation model for UWB signal,which is traveling through the human thorax and is reflected on the air/dry-skin/fat/muscle interfaces,A geometry-based statistical channel model is then developed for simulating the reception of UWB signals in the indoor propagation environment.This model enables replication of time-varying multipath profiles due to the displacement of a human chest.Subsequently, a UWB distributed cognitive radar system (UWB-DCRS) is developed for the robust detection of chest cavity motion and the accurate estimation of respiration rate.The analytical framework can serve as a basis in the planning and evaluation of future rheasurement programs.We also provide a case study on how the antenna beamwidth affects the estimation of respiration rate based on the proposed propagation models and system architecture.

Algorithm for Gesture Recognition Using an IR-UWB Radar Sensor

This paper introduces a human gesture recognition algorithm using an impulse radio ultra-wide- band (IR-UWB) radar sensor. Human gesture recognition has been one of the hottest research topics for quite a long time. Many gesture recognition algorithms or systems using other sensors have been proposed such as using cameras, RFID tags and so on. Among which gesture recognition systems using cameras have been extensively studied in past years and widely used in practical. While it might show some deficiencies in some cases. For example, the users might not like to be filmed by cameras considering their privacies. Besides, it might not work well in very dark environments. While RFID tags could be inconvenient to many people and are likely to be lost. Our gesture recognition algorithm uses IR-UWB radar sensor which has pretty high resolution in ranging and adjustable gesture recognition range, meanwhile, does not have problems in privacy issues or darkness. In this paper, the gesture recognition algorithm is based on the moving direction and distance change of the human hand and the change of the frontal surface area of hand towards radar sensor. By combining these changes while doing gestures, the algorithm may recognize basically 6 kinds of hand gestures. The experimental results show that these gestures are of quite good performance. The performance analysis from experiments is also given.

UWB radar target recognition based on time-domain bispectrum

Complex targets are irradiated by UWB radar, not only the mirror scattering echoes but also the multiscattering interacting echoes are included in target echoes. These two echoes can not be distinguished by classical frequency spectrum and power spectrurm. Time-domain bispectrum features of UWB radar signals that mingled with noise are analyzed, then processing this kind of signal using the method of time-domain bispectrum is experimented. At last, some UW-B radar returns with different signal noise ratio are simulated using the method of time-domain bispectrum Theoretical analysis and the results of simulation show that the method of extraction partial features of UWB radar targets based on time-domain bispectrum is good, and target classification and recognition can be implemented using those features.

Adaptive waveform design based on Morlet wavelet for ultra-wideband MIMO radar

For the issue of deterioration in detection performance caused by dynamically changing environment in ultra-wideband（UWB） multiple input multiple output（MIMO） radar, this paper proposes a novel adaptive waveform design which is aimed to improve the ability of discriminating target and clutter from the radar scene. Firstly, a sequence of Morlet wavelet pulses with frequency hopping and pulse position modulation by Welch-Costas array is designed. Then a waveform optimization solution is proposed which is achieved by applying the minimization mutual-information（MI） strategy. After that, with subsequent iterations of the algorithm, simulation results demonstrate that the optimal waveform design method brings an improvement in the target detection ability in the presence of noise and clutter.

Human Motion Recognition Using Ultra-Wideband Radar and Cameras on Mobile Robot

Cameras can reliably detect human motions in a normal environment, but they are usually affected by sudden illumination changes and complex conditions, which are the major obstacles to the reliability and robustness of the system. To solve this problem, a novel integration method was proposed to combine hi-static ultra-wideband radar and cameras. In this recognition system, two cameras are used to localize the object＇s region, regions while a radar is used to obtain its 3D motion models on a mobile robot. The recognition results can be matched in the 3D motion library in order to recognize its motions. To confirm the effectiveness of the proposed method, the experimental results of recognition using vision sensors and those of recognition using the integration method were compared in different environments. Higher correct-recognition rate is achieved in the experiment.

Performances of Micropower UWB Radar Using Orthogonal Waveforms

Radars and their applications were, for a long time, reserved to national defense, air security or weather service domains. For a few years, with the emergence of new technologies, radar applications have been developed and have become known in the civil domain. In particular, the arrival of UWB—Ultra-Wideband technology allows the design of compact and low-cost radars with multiple fields of application. In this paper, we focus on road applications, such as driving assistance with the objective of increasing safety and reducing accidents. In classical UWB radar systems, Gaussian and monocycle pulses are commonly used. In previous works, original waveforms based on orthogonal functions (Hermite and Gegenbauer) were proposed. These provide a good spatial resolution, suitable for radar detection. Another advantage of these waveforms is their multiple access capability, due to their orthogonality. The aim of the study presented in this article is to compare simulation and experimental results obtained, especially for short-range anticollision radar application, using these waveforms in one part and Gaussian and monocycle pulses in the other part. The originality of this paper relies on the new approach. Indeed, this comparison study using these waveforms has never been done before. Finally, some examples of real experiments in a real road environment with different waveforms are presented and analysed.

Optimization of the UWB Radar System in Medical Imaging

During the last decades, we have witnessed a widespread deployment of the ultra wide band (UWB) radar systems. Considering a medical field, an algorithm optimizing these systems is pointed out in this contribution. Beginning with the description of the UWB radar system, this algorithm has proved to be not only able to take a medical image of the human body but also capable of diverting the human tissue. Moreover, we insist on the fact that this algorithm can easily optimize different radar parameters. So, the human body layer width, the incident angle and the frequency maximizing reflection coefficient are estimated in this paper.

基于环境振动的IR-UWB超感知范围人体跌倒检测方法

跌倒检测在老年人医疗保健中至关重要,传统方案主要依赖于可穿戴设备进行感知.为了避免可穿戴设备带来的负担和不适,采用射频(radio frequency,RF)的非接触式方法因其具有的普适性和非侵入性,逐渐成为一种有前景的替代方案.现有的非接触式方法主要从无线射频信号中提取人体跌倒产生的速度和加速度等运动生理特征,通过分析运动时间序列来判断目标是否发生跌倒行为.虽然目前的方法可以在视距状态下获取较高的检测精度,但是其在感知范围和非视距路径下的感知效果仍存在局限性.为解决以上问题,提出了利用最先进的商用IR-UWB设备来识别超感知范围外的人体跌倒检测方法.该方法的关键是通过识别感知范围外人体跌倒引发的环境振动特征来判断是否发生跌倒事件,并通过设计IQ熵和最远点对距离算法从微小振动中提取人体跌倒产生的信号特征.通过在商用IR-UWB设备上搭建UWFall原型系统并在各种环境下开展大量的实验测试,结果表明,单台收发器的UWFall系统识别准确率超过90%.同时,UWFall系统对7 m范围外的目标跌倒检测准确率超过86%,且在非视距场景下依然保持高鲁棒性.

基于时频域特征融合的IR-UWB穿墙雷达人体行为识别方法

冲激脉冲(impulse radio,IR)超宽带(ultra-wideband,UWB)穿墙雷达因其良好的穿透性和距离分辨率在穿墙人体行为识别领域具有重要作用,但是常规识别方法仅采用单域特征对行为模式进行描述,识别准确率不高。针对这一问题,提出基于时频域特征融合的IR-UWB穿墙雷达人体行为识别算法。首先,通过杂波抑制及距离补偿方法获取高信噪比的人体行为距离像。其次,基于距离像提取目标时域特征,与频域特征进行融合,构建数据集。最后,基于支持向量机(support vector machine,SVM)算法对人体行为进行识别。实验结果表明,所提算法对于IR-UWB穿墙雷达人体行为识别能够达到95%的准确率。

引入时距信息的IR-UWB雷达多域特征融合呼吸模式识别方法

人体呼吸系统相关疾病常常伴随着呼吸深度和节律的异常,因此呼吸信号监测和呼吸模式识别在医疗健康领域中尤其是对于睡眠监测、疾病预断具有重要意义。其中,非接触式的脉冲式超宽带雷达(Impulse Radio Ultra-Wideband,IR-UWB)因具有良好的距离分辨率和穿透能力以及全天候全天时、安全无创的检测优势,正逐步成为睡眠健康监护领域中最关键的感知技术之一。然而受睡眠监测特定的室内场景影响,复杂的测量环境给呼吸模式特征的准确提取带来了限制和挑战,传统的雷达呼吸模式识别算法主要关注一维呼吸时、频域特征,而IR-UWB雷达目标回波信息分散在多个距离门内,使用一维特征识别准确率较低。为此,本文针对IR-UWB雷达中人体呼吸在时间上慢速起伏运动、在距离上是扩展目标的信号模型特点,提出了一种引入时距信息的IR-UWB雷达多域特征融合呼吸模式识别方法。算法在提取一维呼吸信号波形时、频域特征的基础上更进一步挖掘雷达二维时距图像中潜在的呼吸模式形态特征,通过多域特征融合实现呼吸模式的非接触式检测和识别。在图像处理上,针对图像受呼吸异常节律影响呈现局部粘连特性导致呼吸周期提取难的问题,提出一种通过相位矩阵图像处理来检测雷达图像中的呼吸时距条带从而获取图像特征的方法。实验结果表明,利用该算法提取的多域特征对六种呼吸模式进行机器学习的分类识别,可以实现96.3%的识别准确率。

面向矿山救援的UWB雷达生命信息识别研究现状与展望

超宽带(UWB)雷达可穿透煤岩等非磁性介质,实现坍塌物后人员生命信息探测。因矿井环境复杂,加载生命体征信号的UWB雷达探测回波易被环境噪声、杂波信号干扰,且人体目标信息识别困难。介绍了UWB雷达生命探测系统原理及其在矿山救援中的应用。从UWB雷达生命信息提取、动静态人体目标识别和生命体量化3个方面,对UWB雷达生命信息识别研究现状进行了归纳。指出目前UWB雷达生命探测技术在矿山救援领域应用存在的问题:①针对井下坍塌环境中非平稳信号与环境噪声等的滤除方法研究较少。②针对运动(或微动)目标姿势、行为、生命状态等信息的提取与表征方法有待改进,人体生命信息识别模型尚未完善且模型间特征关联性较低。③针对多目标产生的“混叠”问题缺乏解决方案。对面向矿山救援的UWB雷达生命信息识别研究方向作出展望:①不断优化多类矿山灾变环境的噪声与杂波自适应滤除方法。②构建适用于矿山救援领域的人体生命信息识别模型。③进一步提高对矿井遮蔽物后多目标的量化能力。④深入探究UWB雷达最佳探测频段确定方法。

面向钻孔救援的UWB雷达探测技术研究进展

钻孔救援技术在我国已有多起成功救援的案例,但目前用于钻孔救援的探测设备不能满足穿透探测需求,无法利用终孔打偏或造成坍塌的钻孔。UWB雷达具有穿透能力强、抗干扰能力强等诸多特性,可很好地适应灾后矿井环境,实现对障碍物后方的穿透探测。为此,通过文献调研,从UWB雷达定位技术和UWB雷达成像技术两方面总结梳理了基于钻孔救援的UWB雷达探测技术的研究进展。总结出当前UWB雷达定位技术和成像技术在探测方式与装备、探测技术与理论以及回波信息处理技术与算法三方面存在的主要缺陷。针对性地提出了未来研究工作的重点:①研发基于钻孔救援的UWB雷达标识卡定位技术与装备;②结合距离选通技术、改进信息处理程序及CT领域算法研发、优化数据处理技术与算法;③通过雷达阵列分布、ICA算法、改进随机共振算法及融合主流技术构建生命信息识别系统。

UWB MIMO穿墙雷达的阵列设计和成像方法

利用多发多收(MIMO)方式对超宽带(UWB)穿墙成像雷达天线阵列进行稀疏化处理不失为一种很好的选择。该文从成像模型出发,分析了UWB MIMO天线阵的等效接收阵列问题,给出了基于空间卷积理论的UWBMIMO阵列设计方法,仿真验证了方法的有效性和在穿墙成像系统中应用的可行性。此外,提出了相干系数加权稳健Capon波束形成的自适应成像方法,理论分析和实验结果表明该方法能够获得较高的分辨率和图像信噪比。

基于二维映射的LFMUWB雷达动目标速度估计

针对线性调频超宽带雷达动目标存在的距离-多普勒耦合以及多普勒色散对测速的影响,该文采用了频率-多普勒平面的2维映射方法。首先将接收的脉冲串回波,通过快-慢时间的2维傅里叶变换获得相应的频率-多普勒2维平面。然后,利用2维映射处理,将超宽带信号映射为窄带信号,实现多普勒域能量的聚焦。最后,根据映射后的窄带信号对应的多普勒频率获得目标径向速度。该方法不但有效地解决了超宽带雷达多普勒色散对测速的影响,而且规避了由于目标沿距离维的走动引起的慢时间域不能有效的能量积累。通过速度估计值,进行运动补偿后可获得目标距离像。此外,根据相关条件,进一步推导了速度适应范围。仿真实验结果验证了该方法的有效性。

VHF/UHF UWB SAR基于通道均衡的RFI抑制方法

对工作于甚高频/超高频(VHF/UHF)波段的超宽带合成孔径雷达(UWB SAR),射频干扰(RFI)会严重恶化图像质量。该文提出了一种基于通道均衡的RFI抑制方法,它能有效地抑制雷达接收信号中的RFI分量,同时保持图像质量。实际的处理结果证实了该算法的有效性。

基于TH神经网络的UWB-SAR抑制RFI方法

在超宽带合成孔径雷达 (UWB SAR)系统中 ,若将接收信号先通过一个自适应预测误差滤波器 ,然后再进行成像处理运算 ,能极大改善其抑制射频干扰 (RFI)能力 .本文提出了一种迅速、有效的抑制RFI方法 ,它利用谱峰判阶并结合Tank Hopfield(TH)神经网络计算滤波器权系数 ,在保持足够的抑制RFI能力的同时 ,大大提高了运算效率 .

频域多通道UWB-SAR接收机结构分析

分析了一种基于频域多通道的超宽带合成孔径雷达 (UWB SAR)接收机结构。比较了各种滤波器原型在频域分割滤波器组中的性能 ,依据正交镜像滤波器组 (QMF)原理 。

高功率UWB天线阵及其在外场探测中的应用

运用切割式巴仑实现高功率超宽带天线的平衡馈电,并用FDTD进行仿真分析。研制出2×4元高功率天线阵,测量结果显示该天线阵能耐250kV以上高压,工作带宽达6个倍频(200～12 000MHz),具有较好的脉冲波形保真性。利用所研制的天线阵列建立外场探测实验系统,在外场进行目标探测试验。结果表明该天线阵可以用于实际的超宽带雷达发射天线阵,为高功率源在雷达探测中的应用作出了初步探索。