超宽带穿墙雷达动目标跟踪成像算法研究

利用超宽带穿墙雷达探测跟踪墙后人体目标在城区巷战、反恐斗争和人质救援等领域的应用日益广泛。动目标检测与成像算法是穿墙雷达信号处理的两大关键部分,针对传统的动目标检测方法的不足,本文研究了一种提取动目标回波信息的自适应背景去除方法,并考虑到墙体影响,对传统的后向投影成像算法进行改进。利用FDTD数值仿真和外场实验验证了对隐藏在墙后运动目标跟踪成像,研究结果表明,本文算法对天线形式要求简单,准确度高,实时性好。     

混合稳健波束和相干加权的超宽带穿墙雷达目标成像方法

为了提高超宽带穿墙雷达目标成像的分辨率和对比度,提出混合双约束稳健Capon波束形成(DRCB)和相干因子(CF)的自适应成像方法.该方法根据穿墙电磁传播特性首先补偿墙体损耗和传播时延达到天线阵列数据配准的目的,然后利用DRCB提高成像的分辨率和干扰抑制能力,利用改进的CF加权自适应抑制旁瓣提高成像对比度.实验数据处理结果验证了该方法的有效性,与传统成像方法相比可以获得更好的成像结果.     

基于多尺度Fourier-Mellin变换的末制导目标跟踪

针对光电成像末制导阶段目标尺寸迅速增大且可能伴有旋转的稳定跟踪问题,采用了一种基于多尺度Fourier-Mellin变换的目标跟踪方法.根据高斯尺度空间理论和Fourier-Mellin变换,构造了基于多尺度Fourier-Mellin的图像变换参数估计策略.利用平均绝对差分匹配准则对模板和待匹配图像进行图像匹配,当匹配误差过大时,基于多尺度Fourier-Mellin变换进行模板参数估计,求出模板与当前目标之间的尺度和旋转变换参数,并利用双线性内插调整模板,求得目标匹配位置,对模板进行刷新.仿真结果表明,该算法能够适应末制导阶段目标尺寸的急剧变化,实现对目标的稳定跟踪,其跟踪精度和稳定性优于传统方法.     

视觉伺服机器人打击运动目标的轨迹规划

讨论了视觉伺服机器人在打击运动目标时的轨迹规划问题,提出了一种在平面上打击运动目标的方案,包括目标运动参数估计算法、目标跟踪和接近的算法.这种方案适用于打击近似作直线运动目标的轨迹规划.最后给出了一些仿真结果.     

卡尔曼滤波在单目相机运动目标定位中的研究*

单目相机运动目标定位是视觉跟踪的基本任务之一,视觉传感器对目标点位置的获取至关重要。根据单目相机针孔模型,将运动目标图像二维坐标,通过几何关系映射为相机坐标系的三维坐标。标定相机高度和旋转参数,在此基础上求得目标位置坐标。当运动目标被遮挡时,以匀变速运动为模型,根据先前位置信息确定加速度和速度,并对加速度进行自适应更新,建立运动目标状态方程,利用卡尔曼滤波算法预测目标位置。以预估位置代替真实位置,继续估计后续运动状态,在预估位置搜索运动目标,实现运动目标遮挡定位跟踪。实验结果表明,该定位方法具有可行性,当运动目标发生遮挡时,能够完成对目标的定位和跟踪。     

一种高性能的连续波穿墙定位系统研究

基于连续波信号的穿墙雷达探测系统凭借其低廉的成本、简单的结构以及优秀的静态杂波抑制等诸多优势,无论是在军事还是民用领域都受到人们广泛的关注.其中,直达波抑制以及检测过程中的虚假目标识别过滤问题一直是实现连续波穿墙雷达高精度定位的关键技术.针对传统连续波穿墙雷达在上述问题处理上的不足,提出了一种利用负反馈环路实时对消回波信号中的直达波分量,同时根据预先估测的窗函数内噪声幅值大小,自适应地调节系统阈值以去除检测过程中虚假目标的恒虚警检测方法,对雷达性能进行改进.最后用模拟实验的方法验证了提出技术对隐藏在墙后运动目标的检测跟踪能力.实验结果表明,采用提出的新技术对目标进行定位,实时性好,检测精度高,具有较高的实用价值.     

多模式融合的目标跟踪算法

为了解决目标跟踪中运动模型复杂,运动场景多变的情况,提出了一种多模式融合的目标跟踪算法.该算法选取目前广泛应用的mean-shift和粒子滤波算法分别跟踪目标,得到当前目标位置的候选值,并采用加权合成参考函数建立参考模板.然后,以侯选目标位置差异和参考模板为标准,确定目标的正确位置.最后,根据当前帧目标模板和参考模板的距离来决定是否更新模板.实验仿真结果表明,与单一的目标跟踪算法相比,本文算法的平均跟踪误差减小了一倍以上.假如参考模板更新错误,下一帧中仍能以67%的概率正确跟踪目标,连续3次模板更新之后,误更新的模板对目标跟踪的影响可以降低到10%以下,有效地降低了模板更新引起的跟踪错误和跟踪不稳定.     

激光跟踪运动目标伺服系统设计

激光跟踪测量系统实现对运动目标位置检测,要求跟踪机构响应快、动态性能好、精度高.本论文设计了伺服跟踪系统,采用直流力矩电机控制反射镜跟踪目标,实现了对运动目标的高精度快速跟踪.     

基于电子稳像跟踪技术的运动滤波算法

为了在摄像机平台不稳的情况下获取稳定的图像序列,实现基于电子稳像的目标跟踪,对摄像机主动扫描运动与随机抖动分离方法进行了研究,提出了用均值偏移和粒子滤波结合的运动滤波算法(MSPF)来实现运动分离.算法通过粒子滤波预测粒子,然后利用单次均值偏移迭代移动粒子,使粒子更接近于目标真实位置区域,削弱了计算结果精度对粒子数的依赖.对于现实中的复杂背景,利用MSPF算法分离摄像机的主动扫描运动和随机抖动,采用运动补偿图像差分法检测出运动目标,从而实现图像的稳定跟踪.实验结果表明,MSPF算法使用50%的粒子就能起到传统粒子滤波算法同样的效果,缩短了计算时间,有利于实现实时稳像跟踪,适用于车载、船载、机载等稳定跟踪系统中.     

基于PSD的激光跟踪仪光电瞄准技术应用与研究

光电瞄准与定位技术是空间运动目标动态跟踪测量的关键技术,起到目标捕获与运动位置偏差精确指向作用。基于光电位置传感器(PSD)对激光跟踪仪的光电瞄准和跟踪定位控制技术进行了分析研究与设计,提出了光电瞄准控制方案,设计了探测光路,分析了PSD误差修正与信号处理。经过实际样机测试,静态定位测量精度达到6μm,随机动态跟踪测量速度大于1 m/s。     

Experimental study of through-wall human detection using ultra wideband radar sensors

Measurement of weak signals is very challenging since the signals may be buried by noise. In this paper, we study measurement of sense-through-wall signals using UWB radar sensors, and measurements show that the collected signals due to respiratory movement of human target are very weak. Based on the measurements, we perform through-wall human detection. The detection of human targets hidden by walls, or trapped in buildings is of interest for rescue, surveillance and security operations. In this paper, experiments on through-wall human detection using the ultra wideband (UWB) radar PulsOn 220 in monostatic mode are carried out in two scenarios: through gypsum wall, and through wooden door. And three analytic methods are employed for the detection: normalized difference square matrix method, reference moving average method with Discrete Fourier Transform (DFT), and Empirical Mode Decomposition (EMD) from Hilbert Huang Transform, of which the breathing information of the human target is contained in the third intrinsic mode function (IMF3). The experimental results for the human target detection behind the wall are demonstrated and thus compared using these three methods. It shows that the distance of the human target to the UWB radar could be approximately estimated by the normalized difference square matrix method and the reference moving average method. As to Hilbert Huang Transform, even though the existence of the through-wall human target can be clearly detected, the range information is ambiguous.     

浅表层伪随机编码超宽带探地雷达研制

伪随机编码信号因为有着较大的时间带宽积和良好的自相关特性,在探地雷达设计中有着广阔的应用前景。介绍了一套伪随机编码超宽带探地雷达的原理和具体实现方法。基于数字芯片的超宽带信号产生和采样方法,极大地降低了系统设计复杂程度,易于调试。串并转换技术实现了FPGA和高速DAC的数据速率转换。混合采样技术降低了实时采样率,从而降低了采样芯片成本。现场实验结果表明,伪随机编码超宽带信号可以有效地应用于探地雷达,可以在极低功率下实现对地下目标的探测识别。增加编码长度可以在分辨率不变的情况下增加发射能量,从而提高探测性能。     

Target detection and classification by measuring and processing bistatic UWB radar signal

This paper presents a novel method to detect and classify targets obscured by foliage based on real data collected by a bistatic ultra-wideband (UWB) radar system. The type of the target which passes between the transmitter and receiver can have significant effects on the shape of the received waveform. The signal measured by the bistatic UWB radar is related to the type of the target. From these received signals, we extract features that are representative of the target types. Then, we develop target type classification and recognition algorithm based on machine learning techniques. An improved support vector machine (SVM) classifier is developed to perform target types classification and recognition. A novel chaotic differential evolution (CDE) optimization approach using tent map is adopted to determine the parameters of SVM. The effectiveness of the proposed approach is verified by experiments taken in the forest.     

A novel sense-through-foliage target recognition system based on sparse representation and improved particle swarm optimization-based support vector machine

A new recognition system of improved particle swarm optimization-based support vector machine (SVM) combined with sparse representation-based feature extraction is proposed for recognize targets obscured by foliage. Real data sets of four kinds of samples are acquired using a bistatic ultra-wideband (UWB) radar system. Sparse representation (SR) theory is applied to analyzing the components of received target echo signals and sparse coefficients are used to describe target features, the dimension of the sparse coefficients is reduced using principal component analysis (PCA). Support vector machine is a powerful tool for solving the recognition problem with small sampling, nonlinearity and high dimension. Improved particle swarm optimization (IPSO) is developed in this study to determine the optimal parameters for SVM with the highest accuracy and generalization ability. The experiment results indicate that the method of feature extraction using SR can effectively represent the original data better. The recognition result of the proposed method is also compared with SVM, k-nearest neighbor (KNN) and BP neural network (BPNN). The effectiveness of the proposed approach is verified by experiments taken in the forest environment. Our findings show that the proposed method combined with bistatic UWB radar technology provides a good access to achieve the aim of automatic sense through foliage target recognition.     

Mine-Detection Test Facilities at TNO-FEL Site “Waalsdorp”: Some Examples of Results Obtained with an Array Based GPR System

At the TNO Physics and Electronics Laboratory (TNO-FEL), one of the recent research programs explores the use of ultra-wide-band (UWB) electro-magnetic fields in a bi-static ground-penetrating radar (GPR) system for the detection, location and identification of buried items of unexploded ordnance (e.g., land mines). An experimental UWB ground-penetrating radar system was designed to operate in the frequency band from 200 MHz to 3 GHz and uses impulse radiating antennas (IRAs) as transponders to radiate and receive very short electro-magnetic pulses from a short distance above the soil. Part of the development of this UWB GPR system involved the erection of an outdoor tent covered UWB GPR testing facility. In another program an outdoor multi-sensor humanitarian de-mining test facility was constructed. This facility enables semi-automated measurements, with several different sensor on lanes with different soil types. This article highlights the development of the above mentioned system and facilities and presents some results obtained with one of the commercial systems that is present at the site.     

Time-Frequency Signatures of Penetrable or Impenetrable Targets Buried in Lossy Half-Spaces

The capability of ultra-wideband (UWB) radar or sonar systems for extracting signature information useful for target-recognition purposes is well known. The signal processing techniques to be used in the microwave or ultrasonic cases are quite similar. The frequency content of the interrogating signals is usually designed to match the size and type of target and environment present. In the radar case, the complex permittivity of a soil can vary substantially with its moisture content. Both moisture content and target-depth will alter the returned signature. We investigate here the backscattered echoes of impenetrable or penetrable (i.e., dielectric, in this case) bodies of revolution (BOR) of the same size and shape, as they are illuminated by an above-ground projector pointing downwards. The targets are buried at a few representative depths, in soils of various moisture contents. The echoes are simulated by the Method-of-Moments (MoM) method, and then used to determine the target signatures in the time, the frequency, and most importantly, in the joint time-frequency domains. These signal processing methods also apply for the case of ultrasonic signals. In the time-frequency domain the signatures are generated here by a pseudo-Wigner distribution (PWD). Time-frequency distributions can be used for actual target-classification purposes using measured data.     

面向相对导航的UWB测距误差估计与补偿方法

基于无人飞行器和智能机器人等载体构成的智能集群是当前的研究热点,相邻或相近集群成员之间精确的相对距离是密集集群实现协同控制的关键信息。超宽带(UWB)无线传输技术能传输信息,同时具有厘米级的理论测距精度,在集群的协同导航与控制中具有广阔的应用前景。面向集群成员间相对导航的厘米级测距需求,阐述了UWB的测距方案,分析了实际环境中UWB天线的时钟偏移、节点之间有相对速度和非视距环境引起的测距误差的特性,研究了UWB距离测量的状态检测和误差补偿、估计的方法,采用DW1000超宽带模块构建了实验环境,对所研究的方法进行实验验证。结果表明,所研究的方法可显著提高UWB在实际应用中的测距精度,与传统的测距算法相比,在空旷环境(LOS)状态下误差降低了70%,在非视距环境(NLOS)状态下误差降低了50%。     

空间失稳目标线阵激光成像建模及参数优化

线阵激光成像雷达兼具激光测距和线阵成像的优点,是空间失稳目标远距离精密测量的重要工具,其获取的线阵图像既能用于运动估计也能用于空间目标三维测量。为模拟不同平面分辨率线阵激光成像雷达对空间失稳目标的数据采集,本文根据线阵激光成像雷达的成像机制及空间失稳目标的运动规律提出了一般性的空间失稳目标线阵激光成像建模方法,为不同地面验证方案提供了数据支持。同时,为优化雷达参数选取,本文通过计算单位区域特征获取率提出了一种基于数据采集完整性评估的线阵激光成像雷达参数优化方法,实验分析了与成像分辨率相关的性能参数对多种运动形式空间失稳目标数据采集的完整性。实验结果显示数据采集完整性与线阵规模及采样频率正相关,在线阵规模达到100元以上、采样频率200Hz以上,与空间目标自身运动形式几乎无关,建立的对应关系将为参数优化提供指导和参考依据。     

多普勒天气雷达径向干扰回波的识别与消除

多普勒天气雷达在探测气象目标物同时,除了目标物的回波外,一些非目标物的同频电磁波有时也会被散射进入雷达接收系统形成杂波,被称为径向干扰回波。径向干扰回波是污染雷达回波资料的重要因素之一,严重影响雷达资料的定量应用。文章介绍了如何对径向干扰回波进行识别,并给出3种订正方法,即插值法、中值滤波法和多功能插值法,并对这3种方法进行了对比,分析了各种订正方法的优点和局限。实例分析表明通过以上对基数据的处理方法能够对径向干扰回波进行有效的识别和订正,从而获取更准确的雷达基产品及后期导出的二次雷达产品。