Efficient radar target classification using adaptive jointtime-frequency processing

This paper presents a new target recognition scheme via adaptive Gaussian representation, which uses adaptive joint time-frequency processing techniques. The feature extraction stage of the proposed scheme utilizes the geometrical moments of the adaptivity spectrogram. For this purpose, we have derived exact and closed form expressions of geometrical moments of the adaptive spectrogram in the time, frequency, and joint time-frequency domains. Features obtained by this method can provide substantial savings of computational resources, preserving as much essential information for classifying targets as possible. Next, a principal component analysis is used to further reduce the dimension of feature space, and the resulting feature vectors are passed to the classifier stage based on the multilayer perceptron neural network. To demonstrate the performance of the proposed scheme, various thin-wire targets are identified. The results show that the proposed technique has a significant potential for use in target recognition     

LLE-based classification algorithm for MMW radar target recognition

In this paper, a new multiclass classification algorithm is proposed based on the idea of Locally Linear Embedding (LLE), to avoid the defect of traditional manifold learning algorithms, which can not deal with new sample points. The algorithm defines an error as a criterion by computing a sample’s reconstruc-tion weight using LLE. Furthermore, the existence and characteristics of low dimensional manifold in range-profile time-frequency information are explored using manifold learning algorithm, aiming at the problem of target recognition about high range resolution MilliMeter-Wave (MMW) radar. The new algo-rithm is applied to radar target recognition. The experiment results show the algorithm is efficient. Com-pared with other classification algorithms, our method improves the recognition precision and the result is not sensitive to input parameters.     

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     

Orthogonal Discrete Frequency-Coding Waveform design for MIMO radar

Design of orthogonal code sets with ideal correlation properties is crucial for orthogonal Multiple Input Multiple Output（MIMO）radar.A modified Genetic Algorithm（GA）is proposed to numerically design orthogonal Discrete Frequency-Coding Waveforms（DFCWs）with good correlation properties for MIMO radar.Some of the designed results are presented,and their correlation properties are better than those presented in literatures.The effect of Doppler frequency shift on the performance of these signals is simply investigated.Simulation results and comparisons show that the proposed algorithm is more effective for the design of DFCWs with superior aperiodic correlation properties.     

Model-based classification of radar images

A Bayesian approach is presented for model-based classification of images with application to synthetic-aperture radar. Posterior probabilities are computed for candidate hypotheses using physical features estimated from sensor data along with features predicted from these hypotheses. The likelihood scoring allows propagation of uncertainty arising in both the sensor data and object models. The Bayesian classification, including the determination of a correspondence between unordered random features, is shown to be tractable, yielding a classification algorithm, a method for estimating error rates, and a tool for evaluating the performance sensitivity. The radar image features used for classification are point locations with an associated vector of physical attributes; the attributed features are adopted from a parametric model of high-frequency radar scattering. With the emergence of wideband sensor technology, these physical features expand interpretation of radar imagery to access the frequency- and aspect-dependent scattering information carried in the image phase     

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。     

Signal selection in communication and radar systems

A criterion for selecting a finite set of transmitter signals for a continuous communication channel is proposed. The "optimum" signal sets using this criterion are selected to maximize the minimum divergence between hypothesis pairs being tested at the receiver. The resulting signal sets have the property that the error probability using these signals is less than the error probability for any other choice of signals for some {em a priori} message statistics. The signal selection procedure may be applied without a knowledge of the {em a priori} message statistics and does not require an evaluation of error probabilities. Four examples of signal selection are included to illustrate the procedure.     

A practical millimeter-wave radar calibration target

A simple rectangular-plate millimeter-wave radar calibration target gives RCS values ranging from hundreds to thousands of square meters. This enable tests over extended ranges and with equipment having modest output power. Precise mechanical alignment mechanisms and an optical sight are mandatory, due to the narrow beamwidth. Absorber collars provide an easy way of adjusting the actual RCS according to specific mission requirements, and effectively reduce edge-diffraction effects. The measured V-band calibration uncertainty was less than 0.5 dB, and the pointing resolution was better than 0.05/spl deg/.     

雷达目标识别的一种方法

利用目标的方位角、运动速度、俯仰角和雷达散射截面积（RCS），提出了一种雷达目标识别方法。由目标的实测数据，计算特征向量总体的均值和协方差矩辄及其逆矩阵，由距离判别法进行目标识别。识别结果表明，该方法简单、识别效果较好，具有一定的实际应用价值。     

An experimental classification of radar clutter

This letter describes some experimental results concerning the classification of radar clutter-according to whether it is due to reflections from ground, weather disturbances, or migrating flocks of birds. The procedure is based on the maximum entropy method.     

NONPARAMETRIC DETECTION OF RADAR TARGET IN NONHOMOGENEOUS ENVIRONMENTS

This poaper is devoted to the performance evaluation of the Generalized Sigu(GS). Trimmed Generalized Sign(TGS), Modified Savage(MS). Mann-Whitney(MW) and a new proposed detector in multiple target situations. The analysis is carried out for both fluctuating and nonfluctuating received signals. The simulation results show that the new proposed detector has the best detection performance in homogeneous as well as nonhomogeneous background conditions, while TGS procedure is better than the GS detector in distinguishing the primary target from the secondary interfering ones.     

LFMCW雷达高速运动目标检测与估计

针对LFMCW雷达高速运动目标的检测问题,本文提出了一种二次混频与MTD处理相结合的方法。根据上、下调频差拍信号对称特点,采用二次混频处理解决多周期信号中心频率偏移问题,同时实现了距离-多普勒解耦合。MTD处理则等效地实现了多周期信号的积累,并得到目标速度估计。然后根据速度估计值对差拍信号进行运动补偿,获取目标距离信息。性能分析结果表明,与传统MTD方法相比,在输入信噪比一定的情况下,该方法的处理增益不受目标多普勒的影响,因此更适用于高速运动目标的检测。仿真结果验证了该方法的有效性。     

智能化交通信息测量雷达的波形设计及实现

为满足智能化交通发展的需要,分析并设计实现了一种新的多功能交通信息测量雷达的辐射波形。首先简要介绍了测量雷达的功能需求,并以功能需求为出发点展开分析,得出实现这些功能所需的雷达波形;其次,以先进的DDS、PLL、微波倍频和滤波等技术为基础,详细阐述了测量雷达的波形产生方法,并给出了波形产生的软、硬件原理;最后,给出了实测结果,验证了实现方法的准确性。     

Micro-motion target sensing by stepped-frequency continuous-wave radar

Mechanical micro-vibration of a target may induce phase modulations on the radar echo, and the vibration can be detected to identify micro-motion target. In this paper, a new method to detect a mi-cro-motion target and obtain its range and micro-motion frequency is proposed in which multiple periods stepped-frequency continuous-wave signal and Moving Target Indication (MTI) filter are adopted. The simulation results illustrate the validity of this method and present the detection of the target range and the vibration frequency. The experiment gives promising results.     

超宽带探地雷达自动目标识别研究

超宽带探地雷达由于回波信号信息丰富的特点,特征向量的选取成为了自动目标识别的关键.首先利用宽相关处理进行滤波和典型数据提取.提取纵向和横向典型数据用于目标形状识别;提取典型回波道数据进行功率谱分析用于目标材质识别,并利用RBF网络从而实现目标的自动识别.实测数据处理表明:该方法可有效的完成管状物和球状物、铝和铁的识别.     

Measurement and classification of low-grazing-angle radar seaspikes

High-resolution dual-polarization X-band images of the ocean surface were obtained at a grazing angle of about 3°. Area extensive imaging allowed us to study the backscatter properties of sea spikes and to compare radar measurements with visual surface features evident from video recordings. The vertically polarized radar images consist of distributed scatter whose amplitude and Doppler velocity are modulated by larger scale gravity waves consistent with Bragg scattering and composite surface theory (CST). The horizontally polarized radar images are dominated by spatially discrete scattering centers (or sea spikes) moving at velocities comparable to the phase velocities of gravity waves beyond the spectral peak. These sea spikes also exist in the corresponding V-pol radar images, but are less prominent due to the dominant Bragg backscatter. Sea spikes are characterized by polarization ratios H/V that often exceed unity, typically by about 5 dB. Comparison of the larger spikes with simultaneous co-registered video recording of the surface indicates that approximately 30% of observed sea spikes are associated with actively breaking waves (whitecaps) while the remainder are identified with “steep” wave features. By classifying the larger sea spikes according to their corresponding surface features, we find hat the Doppler velocities for sea spikes due to whitecaps are noticeably faster (about 50%) than other sea spikes, though the distributions for both overlap significantly. We also find little measurable difference in the polarization ratios of the two classes of sea spikes as observed on the open ocean     

雷达目标特征提取的一种方法

将小波变换和马氏距离相结合，提出了一种基于ISAR成像的雷达目标特征提取方法。对ISAR成像数据进行正交二进Symlet小波分解和门限处理，对数据进行压缩，然后计算压缩后坐标点的马氏（Mahalanobis)距离，得到目标稳定的特征向量。由实验结果看出，此向量具有在一定范围内的不变性，能够用于雷达目标识别。     

Analysis of target response of FM-CW radar

Detection and location of different targets buried in ground or constructional walls depends to a great extent on the knowledge of expected target return response. The analysis of the target-radar response for FM-CW ground probing radar is performed with the aid of a signal flow diagram relating the target response and the radar performance. A plane wave model for the radar radiated electromagnetic waves is adopted and the analysis is carried out for two typical targets; a finite thickness slab and a sphere. Computed returns based on this analysis are compared with those obtained experimentally from a 1-2 GHz FM-CW radar scheme and a very close agreement is found. A computational study is conducted on the response of planar target model, and important conclusions are reached upon relevant to ground probing problem.     

基于恒虚警的高频地波雷达低速目标检测研究

海杂波背景下的高频地波雷达低速移动目标检测比较复杂,单独运用某种检测方法往往无法有效检测目标且导致很高的虚警概率.基于目标在距离多普勒谱图上的特征,提出了结合峰值检测技术、并同时在频域的多普勒向和距离向进行恒虚警检测的三级目标检测方案,进而利用不同海态等级下的模拟海杂波对该三级目标检测方案的性能进行仿真分析,仿真结果均符合理论上的整体虚警率分析并得到了较高的检测概率.最后该方法成功用于武汉大学的OSMAR高频地波雷达的目标实验数据,显示了较高的工程应用价值.     

A review of target decomposition theorems in radar polarimetry

In this paper, we provide a review of the different approaches used for target decomposition theory in radar polarimetry. We classify three main types of theorem; those based on the Mueller matrix and Stokes vector, those using an eigenvector analysis of the covariance or coherency matrix, and those employing coherent decomposition of the scattering matrix. We unify the formulation of these different approaches using transformation theory and an eigenvector analysis. We show how special forms of these decompositions apply for the important case of backscatter from terrain with generic symmetries