一种基于超宽带雷达的日常活动分类方法

近年来,随着智能物联网应用的快速发展,雷达传感器由于具有保护隐私、全天候全天时工作、不受光线和遮挡影响等优点,在人体目标日常行为活动识别方面受到了学术界和产业界的极大重视.针对一种超低辐射的超宽带雷达(Impulse Radio Ultra-Wideband,IR-UWB),提出了一种室内人员日常活动(包含静止、坐下、...     

A new approach for classification of human gait based on time-frequency feature representations

We introduce a new and simple technique for human gait classification based on the time-frequency analysis of radar data. The focus is on the classification of arm movements to discern free vs. confined arm swinging motion. The latter may arise in hostage situation or may be indicative to carrying objects with one or both hands. The motion signatures corresponding to the arm and leg movements are both extracted from the time-frequency representation of the micro-Doppler. The time-frequency analysis is performed using the multiwindow S-method. With the Hermite functions acting as multiwindows, it is shown that the Hermite S-method provides an efficient representation of the complex Doppler associated with human walking. The proposed human gait classification technique utilizes the arm positive and negative Doppler frequencies and their relative time of occurrence. It is tested on various real radar signals and shown to provide an accurate classification.     

Noncooperative target classification using hierarchical modeling ofhigh-range resolution radar signatures

The classification of high-range resolution (HRR) radar signatures using multiscale features is considered. We present a hierarchical autoregressive moving average (ARMA) model for modeling HRR radar signals at multiple scales and use spectral features extracted from the model for classifying radar signatures. First, we show that the radar signal at a different scale obeys an ARMA process if it is an ARMA process at the observed scale. Then, an algorithm to estimate model parameters and power spectral density function at different scales using model parameters at the observed scale is presented. A feature set composed of spectral peaks is extracted from the estimated spectral density function using multiscale ARMA models. For HRR radar signature classification, multispectral features extracted from five different scales are used, and a minimum distance classifier with multiple prototypes is used to classify HRR data. The multiscale classifier is applied to two HRR radar data sets. Each data set contains 2500 test samples and 2500 training samples in five classes. For both data sets, about 95% of the radar returns are correctly classified     

UWB-HA4D-1.0:超宽带雷达人体动作四维成像数据集

雷达人体行为感知系统具有穿透探测能力,在安防、救援、医疗等领域具有广泛的应用前景.近年来,深度学习技术的出现促进了雷达传感器在人体行为感知领域的发展,同时对相关数据集的样本规模和丰富性提出了更高的要求.该文公开了一个超宽带雷达人体动作四维成像数据集,该数据集以超宽带多输入多输出雷达为探测传感器来获取了人体目标的距离-方...     

Automatic modulation classification of noise-like radar intrapulse signals using cascade classifier

Automatic modulation classification is essential in radar emitter identification. We propose a cascade classifier by combining a support vector machine (SVM) and convolutional neural network (CNN), considering that noise might be taken as radar signals. First, the SVM distinguishes noise signals by the main ridge slice feature of signals. Second, the complex envelope features of the predicted radar signals are extracted and placed into a designed CNN, where a modulation classification task is performed. Simulation results show that the SVM-CNN can effectively distinguish radar signals from noise. The overall probability of successful recognition (PSR) of modulation is 98.52% at 20 dB and 82.27% at −2 dB with low computation costs. Furthermore, we found that the accuracy of intermediate frequency estimation significantly affects the PSR. This study shows the possibility of training a classifier using complex envelope features. What the proposed CNN has learned can be interpreted as an equivalent matched filter consisting of a series of small filters that can provide different responses determined by envelope features.     

基于支持向量机的相控阵雷达信号识别方法

在分析支持向量机识别原理和相控阵雷达信号特点的基础上,确定了用于分类识别的雷达特征参数,并给出了采用支持向量机来实现相控阵雷达信号识别的具体方法。仿真结果表明,使用一对一算法和多项式核函数的支持向量机分类器的方法对相控阵雷达信号的识别效果较好。     

基于中心矩特征的空间目标识别方法

目标的雷达散射截面（RCS）包含了丰富的目标类别信息,有效地利用目标RCS特征对空间目标的雷达识别具有重要的意义。该文利用空间目标回波的距离维信号来进行识别。中心矩特征具有平移不变性,是一种简单有效的波形特征提取算法。文中首先提取中心矩作为特征向量,再采用Fisher判据进一步进行特征压缩,最后利。用支撑矢量机（SVM）分类算法实现识别。基于实测数据的仿真实验结果表明,该方法具有较好的识别性能和推广能力。     

基于微多普勒特征的人体步态参数估计

基于雷达的人体微动研究是近年发展起来的新技术,通过信号处理手段可获取人体走动时手和腿的摆动激励的多普勒频移,用于人体步态探测与识别。本文首先分析了人体行走对雷达波的调制,介绍了一套用于人体运动特性测量的X波段连续波雷达系统及测量实验,并对人体行走实测数据中微多普勒特征进行分析,然后深入研究了P.van Dorp等人提出的基于微多普勒特征的人体行走实测数据特征提取和参数估计的方法,并将之应用于本雷达系统的测量结果中,最后给出了仿真结果。      

基于实测数据的空中目标分类识别算法

该文在分析由常规窄带雷达获得的直升机、螺旋桨和喷气式飞机实测回波数据特征的基础上,提出一种基于多特征联合的分类识别算法。通过对大量实测回波数据的特征分析,提取多普勒频移、幅度相对量、时域和频域波形熵、时频特征多个具有明显区分性的特征,将其输入支撑向量机(SVM)分类器实现3类空中目标的分类。在分类的基础上,基于回波数据的时频谱宽和对称性特征,提出一种奇数与偶数片桨叶直升机识别方法。最后实测数据的处理结果验证了所提空中目标分类识别方法的有效性。     

MDI训练样本集构建对雷达探测旋翼无人机分类的影响分析

利用卷积神经网络对目标微多普勒特征进行深度学习是目前雷达探测无人机分类的重要手段.实际应用中,无人机参数如叶片转速、叶片长度、叶片初始相位、无人机方位角、无人机俯仰角、无人机径向速度等参数变化大,导致训练样本变化大.该文分析训练样本集对旋翼无人机分类结果的影响.首先建立单旋翼无人直升机、四旋翼无人机和六旋翼无人机雷达回...     

基于FMCW雷达的人体复杂动作识别

针对利用摄像机进行人体动作识别时易受视距和光线影响等问题,提出一种基于FMCW雷达的人体复杂动作识别方案。首先基于FMCW信号模型对雷达采样数据采用一种以RDM（Range Doppler Map）向速度维投影的方式逐帧构建微多普勒谱图,继而基于微多普勒谱图来提取用于表征整个动作频谱相关信息的8种特征矢量。最后,基于雷达实测数据,以贝叶斯超参数调整优化后的支持向量机作为分类器,分析利用所提取的单特征矢量以及特征矢量组合来进行分类时对分类准确率的影响,用以筛选最优异的特征矢量组合。实验结果表明,从微多普勒谱图中所提取的特征矢量皆可直观地表述整个动作过程的特性,且利用最终筛选得到的最优异的特征矢量组合对已知个体和未知个体的9种动作进行识别,识别准确率分别高达99.07%和96.76%。     

基于微多普勒特征的单人与小分队分类技术

人体行走是典型的非刚体运动,通常情况下每个人行走时的摆动周期不可能完全相同。通过提取目标频谱归一化幅度和、多普勒谱线数和谱宽的标准差3个典型特征,采用支持向量机( SVM)分类器,实现了短驻留时间条件下单人与多人的有效鉴别,平均识别率大于90%。雷达实测数据表明所提特征是有效并且稳健的。     

行进人体目标雷达瞬时多普勒特征分析

行进人体目标对雷达回波产生多普勒调制和微多普勒调制,提取目标雷达回波中的多普勒和微多普勒特征,可获得人体目标的运动速度及肢体摆动周期等信息,有助于人体目标的检测与识别。本文改进和完善了行进人体目标的线性刚体模型,得出连续波信号体制下人体目标雷达回波,推导出人体目标的瞬时多普勒频率,定性分析了目标的瞬时多普勒特征和瞬时运动特征的变化规律。利用公开的实测数据验证了本文模型的有效性,并利用时频分析工具对目标雷达回波的瞬时特征进行了仿真分析,提取出目标的瞬时运动特征。本文模型及特征提取方法可推广应用于其他信号体制的雷达回波信号分析,同时可推广应用于对目标其他运动状态的分析。      

LSTSVM的样本缩减与空间信息融合方法

针对最小二乘孪生支持向量机(STSVM,least squares twin support vector machines)分类效率低的不足,在一对余(1-a-r)多分类器的基础上,提出一种基于样本缩减(SR)的LSTSVM(SRLSTSVM)分类算法。在核空间中通过距离计算,选出对分类超平面起决定作用的样本点,用于分类器的训练;与此同时,为了充分利用高光谱遥感图像的空间信息,通过主成分分析(PCA)和二维Gabor滤波获取像元的纹理特征,将高光谱遥感图像的空间信息和光谱信息在图像层进行融合用于分类。实验证明,本文提出的SR算法可以在不影响分类精度的基础上大大提高LSTSVM的分类效率,且结合空间信息后的LSTSVM的总体分类精度也有明显提高。     

Support Vector Machine and Neural Network Classification of Metallic Objects Using Coefficients of the Spheroidal MQS Response Modes

Two different supervised learning algorithms, support vector machine (SVM) and neural networks (NN), are applied in classifying metallic objects according to size using the expansion coefficients of their magneto-quasistatic response in the spheroidal coordinate system. The classified objects include homogeneous spheroids and composite metallic assemblages meant to resemble unexploded ordnance. An analytical model is used to generate the necessary training data for each learning method. SVM and NN are shown to be successful in classifying three different types of objects on the basis of size. They are capable of fast classification, making them suitable for real-time application. Furthermore, both methods are robust and have a good tolerance of 20-dB SNR additive Gaussian noise. SVM shows promise in dealing with noise due to uncertainty in the object's position and orientation.     

Classification of Convective and Stratiform Cells in Meteorological Radar Images Using SVM Based on a Textural Analysis

This contribution deals with the discrimination between stratiform and convective cells in meteorological radar images. This study is based on a textural analysis of the latter and their classification using a support vector machine (SVM). First, we apply different textural parameters such as energy, entropy, inertia, and local homogeneity. Through this experience, we identify the different textural features of both the stratiform and convective cells. Then, we use an SVM to find the best discriminating parameter between the two types of clouds. The main goal of this work is to better apply the Palmer and Marshall Z-R relations specific to each type of precipitation.     

融合时空上下文的手绘笔画图文分类

在笔式用户界面中,对手绘图形和手写文字的识别通常采用不同的识别算法,因此通过笔画分类将混杂的笔画集自动分离是手绘草图识别中的一个重要研究课题。该文提出一种融合时空上下文的手绘笔画联合分类方法,采用支持向量随机场对时空关联的笔画集进行联合建模,不仅利用笔画自身的特征进行判别分类,还以时空邻域和笔画对特征同时融合了笔画间的时间上下文和空间上下文信息,通过模型环状置信传播(LBP)推断,最终求得最大后验边缘概率准则下的联合分类结果。实验结果表明,该文方法的分类准确率优于基于SVM的单笔画分类方法和基于马尔科夫随机场(MRF)的空间上下文联合分类方法,分类速度能基本满足交互实时性的要求,验证了利用随机场模型融合时空上下文进行笔画分类的可行性和有效性。     

低分辨机载雷达飞机目标分类识别技术研究

目标分类识别技术是机载雷达当今和未来发展的重要需求之一。目前研究较多的是基于宽带信号的目标识别,对雷达系统和目标信噪比提出了较高的要求,且对角度较敏感。考虑到现有机载雷达大多具有窄带跟踪模式和目标分类识别的需求背景下,该文提出了一种基于窄带分形和幅度调制联合2 维特征的飞机目标分类识别方法。文中以支持向量机(SVM)为分类器,以实测试飞数据进行分析验证,试验结果表明,该方法能有效分类识别直升机、螺旋桨飞机、喷气式飞机,平均分类识别率在92%以上。      

基于DSP的雷达信号分选实用技术

基于在密集的雷达信号环境进行信号分选是一个难题，介绍了一种实用的雷达信号分选技术，给出了基于数字信号处理（DSP）的硬件设计方案，在比较了CDIF及SDIF算法的基础上详细说明了如何实现对信号的有效分选。可分选出的信号有抖动信号、重频参差、频率捷变及频率分集信号等。该设计已经成功运用于某雷达对抗侦察系统，试验效果较好。     

概率隐含语义分析模型在行为识别中的编码与归一化方法研究

在视频中的行为识别的语境下,为了提高概率隐含语义分析模型的识别性能,研究了不同编码方法结合归一化方法对于分类性能的影响;还考察了主成分分析预处理原始特征对于性能的影响,在显著降低特征维度进而降低计算量的同时,当特征包含较多噪声成分的情况下性能甚至会有所提升。在KTH和UT-interaction 数据库上的实验表明,编码和归一化方法的适当组合可以显著提高模型的性能。在UT-interaction数据库的两个子集上识别精度分别达到了当前最好的结果96.44%、95%,其中在数据集1上采用稀疏的时空兴趣点特征,得到了94.24%的识别精度。