High resolution radar target modeling using a modified Pronyestimator

A method for characterizing radar signatures using a Prony model is developed based on the concept of scattering centers. A parameterization of the Prony model specific to the radar target identification problem is chosen and several key components to the algorithm, including the use of singular value decomposition and the removal of spurious scattering centers, are presented. The resulting algorithm is tested with data taken from a compact range. These tests include comparison of different targets, different aspect angles and frequency ranges, as well as robustness tests on the algorithm and evaluation of performance in noise     

High resolution parametric modelling for two-dimensional radar target using prony algorithm

On the conditions of low-resolution radar, a parametric model for two-dimensional radar target is described here according to the theory of electromagnetic scattering and the geometrical theory of diffraction. A high resolution estimation algorithm to extract the model parameters is also developed by building the relation of the scattering model and Prony model. The analysis of Cramer-Rao bound and simulation show that the method here has better statistical performance. The simulated analysis also indicates that the accurate extraction of the diffraction coefficient of scattering center is restricted by signal to noise ratio, radar center frequency and radar bandwidth.     

High resolution 3D imaging in MIMO radar with sparse array

High resolution three-dimensional (3D) imaging method using MIMO radar with sparse array is studied in this paper. A method based on compressive sensing (CS) is firstly given. However, the CS-based method has the off-grid problem which will reduce the estimation accuracy of scatterers’ position on the target. Moreover, a high dimensional measurement matrix is required in the CS-based method, which will lead to a heavy storage and computation burden. To solve the two problems of CS, a new method based on matrix completion is proposed in this paper. After reshaping the sparse 3D echo into a low-rank structured matrix, the full 3D echo can be recovered by solving a nuclear norm minimization problem. Then the accurate position of scatterers can be estimated by applying multi-dimensional harmonic retrieval methods to the full 3D echo. Finally, the high resolution 3D image of targets is reconstructed. The effectiveness of the method is validated by the results of comparative simulations.     

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     

基于联合概率加权的高分辨雷达目标点迹处理

针对高分辨雷达在跟踪扩展目标时出现多散射点的问题,提出了一种基于联合概率加权的雷达目标点迹处理方法。首先,对信号检测出的多散射点构建其残差变量,根据判别准则进行目标点迹配对处理,剔除异常散射点;然后,同时考虑散射点位置和回波幅度信息,计算联合概率的权值,目标多散射点经概率加权融合后估计得到散射中心。仿真分析表明,该方法在高分辨雷达扩展目标点迹处理中能有效抑制杂波的干扰,目标点迹精度在距离、方位和俯仰上分别提高了20.63%、47.51%和41.03%,同时具有很好的可靠性,满足工程应用的需求。     

High resolution estimation for sub-gaussian stable signals in a linear array model

The authors describe a high resolution subspace fitting (HRE) algorithm for robust estimation of direction of arrival (DOA) in the uniform linear array model with heavy-tailed signals and noise. Electromagnetic disturbances on telephone lines, atmospheric noise and underwater acoustic noise often exhibit heavy-tailed behaviour with differing characteristics. Although statistical models under Gaussian assumptions of signals and noise have been extensively investigated in the literature, there is limited research on robust methods in the non-Gaussian setting. A general model with sub-Gaussian alpha-stable signals is described, which includes the isotropic alpha-stable, and independent and dependent Gaussian models as special cases. It is shown that the HRE algorithm provides strongly consistent estimates of the DOAs. In addition, Monte Carlo simulation studies show that the proposed algorithm works extremely well for closely spaced targets, and outperforms the multiple signal classification-type algorithms for strongly dependent signals, both in the stable and the Gaussian cases     

双辨别子空间高分辨距离像雷达目标识别

针对飞机目标的分类问题,提出了一种双辨别子空间高分辨距离像雷达目标识别方法.该方法首先依据Fisher准则导出距离像总散布矩阵的零空间中不含有辨别信息的结论,利用这一结论,对类间和类内散布矩阵进行预降维,降低了后续计算的复杂度.从全局的角度出发,基于类内散布矩阵零空间与非零空间所包含的辨别信息分别建立辨别子空间,实现对目标的特征提取.对ISAR实测飞机数据进行了分类,并与经典子空间方法进行比较,结果表明所提算法有效改善了目标识别性能.     

Minimum norm target estimation algorithm for phased array radar

An algorithm is proposed that uses a broadside phased array to profile any number of arbitrary targets as a function of bearing. The technique places no restriction on either the number or location of the array elements.<>     

基于旋转多元阵列探测器的快速光声成像系统

为了快速得到高质量的光声重建图像,采用旋转多元线性阵列探测器,可改善投影不均匀的影响,重建复杂吸收体的光声像。由旋转不同角度数的光声像的仿真和实验表明,利用多元线性阵列探测器旋转扫描的光声像,能够更好地反映生物组织的光学吸收分布。该系统有望为组织的在体功能成像提供一种更方便快捷的装置和方法。     

外辐射源雷达目标旋转部件微动参数估计

大多数飞机目标具有旋转部件, 对其微动参数进行估计可为雷达目标分类识别提供重要信息.外辐射源雷达因其体制上的特点, 在微多普勒效应探测及其利用等方面具有独特优势.文中结合压缩感知理论提出了一种目标旋转部件微动参数估计新方法, 该方法基于微多普勒信号内在特性来构造字典矩阵, 将常规的微动参数估计问题转化为稀疏信号恢复问题.仿真与实测数据处理结果验证了所提方法的有效性和鲁棒性.     

用辐射分辨率分析伪装目标的雷达发现概率

利用雷达图像辐射分辨率理论讨论目标发现概率计算方法.依据辐射分辨率的定义及图像检测灰度对比要求,计算给出了某单一背景条件下分布伪装目标与背景对比度、独立视数及目标发现概率的关系.     

雷达目标一维距离像识别中的非训练目标判别

针对雷达目标一维距离像识别中的特征子空间法不能判别训练目标的缺点，提出一种非训练目标判别门限，在特征子空间法的分类阶段对非训练目标进行判别。仿真实验结果表明：在特征子空间法中引入该门限，既能有效地对非训练目标予以判别，同时对训练目标又能保持较高的正确识别率。     

基于高分辨率一维距离像雷达目标识别研究

基于一维距离像的目标识别是雷达目标识别的一种重要方法。本文利用最大相关系数法（MCC）和主分量分析方法（PCA）对目标一维距离像进行目标识别。针对一维距离像的目标姿态敏感性,分析了最大相关系数法和PCA特征提取方法的原理,并通过3种目标的实测数据进行分类实验,表明该算法的有效性。     

分布式MIMO雷达的参数估计与检测联合算法

本文针对分布式MIMO雷达系统,在站间大间隔配置获得的空间分集增益的基础上,提出了一种目标位置估计与检测的联合算法。与以往距离门检测不同的是,这里通过所定义的目标假设框架下进行联合估计与检测。通过理论分析证明,本文所提出的位置估计与检测联合算法在检测性能上要优于距离门检测法,且漏检概率与信噪比SNR成反比。仿真实验也验证了算法的有效性。     

双基多输入多输出雷达快速目标空间定位算法

提出了一种基于双基地多输入多输出（MIMO）雷达的快速目标空间定位算法。该算法通过双基地MIMO雷达单周期回波形成的虚拟阵列数据来构造三个矩阵,然后用传播算子法分别对其进行处理得到了三个具有特定关系的矩阵,利用矩阵的特征值和特征向量导出了目标角度的闭式解,从而可实现对目标的空间定位。该算法避免了数据协方差矩阵的估计,不需要多维谱峰搜索和参数配对,大大降低了运算复杂度。计算机仿真结果证明了所提算法的正确性和可行性。     

线性调频连续波雷达多目标分辨的新方法

为克服传统的线性调频连续波(LFMCW)雷达在多目标分辨中的局限性,分析了LFMCW雷达差拍信号频谱特点,提出了一种基于二次混频和"CLEAN"技术的新方法,解决了多目标分辨中虚假目标问题.计算机仿真结果显示当检测信噪比在-10dB以下时仍能正确识别各个目标,表明了算法的有效性.     

拖曳线列阵声呐目标运动分析数学模型

研究了质心与形体拖曳线列阵目标运动分析;对于质心拖曳线列阵,在论证量测原理误差基础上,分别给出质心拖曳线列阵单目标与多目标运动分析的数学模型;对于形体拖曳线列阵单目标运动分析(STMA),在不含时延及变化率以及含时延及变化率两种情况下,给出了目标运动分析的数学模型;对形体拖曳线列阵多目标运动分析(MTMA),在不含时延及其变化率情况下,给出目标运动分析的数学模型.     

Scenario and target simulation for a ground based multifunction phased array radar

This paper describes a scenario and target simulation which operates in non real-time to provide full closed-loop operation of the ground based multifunction phased array radar simulation system in support of ballistic missile defence experiments against countermeasure. By simulating the target scattering signature and dynamical signature, this scenario and target simulation provide realistic scenario source to evaluate the system performance of multifunction phased array radar, and the key algorithms verification and validation such as target tracking, multi-target imaging and target recognition.     

一种弹载线阵激光雷达距离像噪声的抑制算法

为了得到更加可靠的激光雷达距离像,充分考虑弹载激光雷达的特殊工况,提出一种基于扫描路径线的距离反常噪声抑制算法。首先,采用线形滤波窗口对原始距离像每一个像素进行距离反常多次判定,分别对当前像素是否为中值、与中值的差值是否大于阈值以及当前像素的绝对变化幅值进行分析;然后,在判定为距离反常值之后,在滤波窗口内进行二次中值滤波,前后相比较之后做出决断。实验结果表明:在RMES(均方根误差)的判定条件下,此算法在不同高度、不同坡形、不同角度的多组数据情况下,对距离像的距离反常噪声抑制效果,都要明显优于传统滤波窗口为3×3和5×5的中值滤波;并且算法处理速度较快,能够满足初步的应用需求。     

末敏弹线阵列激光雷达的距离像分割方法

为了增强末敏弹在不同场景下对地面装甲目标的探测识别性能,充分考虑末敏弹弹载线阵激光雷达的应用背景,提出了综合交叉扫描线法和梯度连通域的地物距离像点云分割算法,用以提高对地面和目标的分割效果。首先,将激光雷达扫描得到的原始距离信息转换为距离水平地面高度值,通过交叉扫描线法将空间斜面转化至水平面,增强不同地形上的适应性;然后,采用地面点云连通域算法提取地面点云和形态学梯度阈值法分割得到目标点云;最后计算了地物分割效果的几何相似度。实验结果表明:该算法对于正斜坡、侧斜坡等多种地形都有较好的适用性,在不同高度、地形、坡度都能够准确、有效地分割地物点云,进而提高末敏弹对装甲目标的识别性能。