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     

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.     

Waveform selection in radar target classification

We apply a sequential experiment design procedure to the problem of signal selection for radar target classification. Radar waveforms are designed to discriminate between targets possessing a doubly spread reflectivity function that are observed in clutter. The waveforms minimize decision time by maximizing the discrimination information in the echo signal. Each waveform selected maximizes the Kullback-Leibler (1951) information number that measures the dissimilarity between the observed target and the alternative targets. We discuss in details two scenarios. In the first scenario, the target environment is assumed fixed during illumination. In this case, the optimal waveform selection strategy leads to a fixed library of waveforms. During actual classification, the sequence in which the waveforms are selected from the library is determined from the noise to clutter power in the range-Doppler support of the targets. In the second scenario, the target environment changes between pulse transmissions. In this case, the maximum discrimination information is obtained by a repeated transmission of a single waveform designed from the reflectivity function of the targets. We show that our choice of signals can produce significant gains in detection performance     

雷达系统误差的测量和修正方法

在分析雷达系统误差特性的基础上,根据工程经验,提出雷达系统误差的测量方法、计算方法、置信度分析和修正方法,并给出实例。     

PSA频谱分析仪测量应用

精确测量信号功率电平是现 代通信系统中的重要技术环节。无论是对电路子系统还是对整个通信链路进行分析时,技术指标和容限均十分重要。 与传统的频谱分析仪相比,Ａｇｉｌｅｎｔ高性能频谱分析仪（ＰＳＡ）系列体现出多方面的技术创新──精密的平坦度校准、全数字中频（ＩＦ）部分和内部校准器,可以达到最佳的幅度精度,使测量更精确、更迅速和更方便。 根据Ａｇｉｌｅｎｔ ＰＳＡ和８５６３Ｅ幅度精度指标的比较,可以得出ＰＳＡ系列的幅度不确定性远小于８５６３Ｅ频谱分析仪的结论。当将ＰＳＡ系列与市场上的其它频谱分析仪作比较时…     

Evaluation of textural and multipolarization radar features forcrop classification

The aim of this research is to evaluate crop discrimination using airborne radar data based on multipolarization and textural information. Multipolarization data (C-HH, C-VV, and C-HV) were used for discriminating 5 crop types i.e., corn, wheat, soya, pasture, and alfalfa. For the multipolarization evaluation, an unsupervised classification algorithm and a supervised method based on maximum likelihood were used on the data. For the textural evaluation, textural measures of different degrees were calculated on three different order histograms and were evaluated from the crop discrimination point of view. Results show that multipolarization correct classification rates of 86.31% and 74.47% were obtained for supervised and unsupervised methods respectively. Hence, multipolarization radar data offer an adequate tool for crop identification especially with supervised classification. The evaluation of textural measures shows that for a first order histogram the mean measure gives the best rate of discrimination. In the case of second and third order histograms, the best measures are contrast and large number emphasis respectively. These textural measures were integrated with the three multipolarization channels in order to determine their specific contributions. Results show that crop class separability is thereby improved and that the rate of correct classification increased by 9.79% for the crops     

Application of neural networks to radar image classification

A number of methods have been developed to classify ground terrain types from fully polarimetric synthetic aperture radar (SAR) images, and these techniques are often grouped into supervised and unsupervised approaches. Supervised methods have yielded higher accuracy than unsupervised techniques, but suffer from the need for human interaction to determine classes and training regions. In contrast, unsupervised methods determine classes automatically, but generally show limited ability to accurately divide terrain into natural classes. In this paper, a new terrain classification technique is introduced to determine terrain classes in polarimetric SAR images, utilizing unsupervised neural networks to provide automatic classification, and employing an iterative algorithm to improve the performance. Several types of unsupervised neural networks are first applied to the classification of SAR images, and the results are compared to those of more conventional unsupervised methods. Results show that one neural network method-Learning Vector Quantization (LVQ)-outperforms the conventional unsupervised classifiers, but is still inferior to supervised methods. To overcome this poor accuracy, an iterative algorithm is proposed where the SAR image is reclassified using a maximum likelihood (ML) classifier. It is shown that this algorithm converges, and significantly improves classification accuracy     

探地雷达回波信号的特征提取和分类

探地雷达的回波信号具有非平稳特征.为实现非平稳信号的检测和分类,采用了二进小波变换、主分量分析与Fisher线性判别分析和前馈多层感知器分类器分别对探地雷达回波信号进行小波分解、去相关与特征选择和分类.实验结果表明,该算法具有很好的分类效果.     

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     

动态雷达目标RCS测量及分析

根据动态雷达目标RCS测量原理，结合RCS测量雷达外场试验，确定了动态测量方法、飞行航线、测量参数和数据处理方法，给出了对某飞行目标的实测结果，并与仿真结果进行了对比分析，为内场精确仿真动态飞行目标的RCS提供了依据。     

Measurement of target coordinates in three-dimensional bistatic forward-scattering radar

The maximum-likelihood technique is applied to determine the coordinates of moving targets in a three-dimensional bistatic forward-scattering radar. The potential accuracy of the coordinates’ determination is estimated. Simulation results are presented.     

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     

Numerical simulation of low-grazing-angle ocean microwavebackscatter and its relation to sea spikes

This paper presents the results of numerically simulating microwave backscatter from a deep-water breaking wave profile. Enhanced microwave backscatter from the crests of breaking waves has been hypothesized as the source of bright short-lived microwave radar echoes that are observed at low-grazing angles (LGAs). The characteristics of these “sea spikes” are distinctly different from the Bragg-scatter echoes that dominate measurements made at moderate grazing angles. Of particular interest is the high contrast that sea spikes present against ocean background backscatter when observed with horizontally polarized transmit/receive configurations [horizontal (HH) versus vertical (VV)]. This HH/VV contrast disparity has been attributed to polarization-selective cancellation of the direct reflection from the wave crest by the surface reflection. This hypothesis is reinforced first by showing evidence that VV polarization is suppressed in the intensity range that would normally be populated by the brightest scatterers. Histograms of unaveraged Doppler-centroid measurements show further that the depleted VV backscatter population is responding to scatterers that are moving much more slowly than the HH scatterers. The Doppler-centroid histograms provide a sharper delination between the two scattering populations than do the unconditionally averaged Doppler spectra that are more commonly reported. Finally, our numerical simulations show evidence of an interference mechanism that selectively suppresses VV backscatter. In our simulations, the polarization selectivity comes from the phase dependence of the backscatter from the wave crest. A Brewster phenomenon at the surface reflection point is not necessary     

Measurement matrix design for CS-MIMO radar using multi-objective optimization

In this paper, we design a measurement matrix for a compressive sensing-multiple-input multiple-output radar in the presence of clutter and interference. To optimize the measurement matrix, three main criteria are considered simultaneously to improve detection and sparse recovery performance while suppressing clutter and interference. To this end, we consider three well-known criteria including Bhattacharyya distance, mutual coherency of sensing matrix, and signal-to-clutter-plus-interference ratio. Due to the use of simultaneous multi-objective functions, a multi-objective optimization (MOO) framework is exploited. Some numerical examples are provided to illustrate the achieved improvement of our proposed method in target detection and sparse recovery performance. Simulation results show that the proposed MOO technique for measurement matrix design can achieve superior performance in target detection compared with Gaussian random measurement matrix technique.     

基于点云数据分类的机载雷达地形坡度勘测

为提高变电站的三维设计效果,需勘测变电站地形坡度.提出基于点云数据分类的机载雷达地形坡度勘测方法.采用机载雷达回波扫描技术采集地形坡度勘测数据.结合像素点跟踪识别法,重构地形坡度勘测过程中三维图像.采用三维光学检测技术采样机载雷达地形坡度勘测点云数据,结合中值滤波法和平均值滤波方法,增强地形坡度点云信息分类结果,获取地...     

Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields

Due to the enormous quantity of radar images acquired by satellites and through shuttle missions, there is an evident need for efficient automatic analysis tools. This paper describes unsupervised classification of radar images in the framework of hidden Markov models and generalized mixture estimation. Hidden Markov chain models, applied to a Hilbert-Peano scan of the image, constitute a fast and robust alternative to hidden Markov random field models for spatial regularization of image analysis problems, even though the latter provide a finer and more intuitive modeling of spatial relationships. We here compare the two approaches and show that they can be combined in a way that conserves their respective advantages. We also describe how the distribution families and parameters of classes with constant or textured radar reflectivity can be determined through generalized mixture estimation. Sample results obtained on real and simulated radar images are presented.     

Hydrometeor classification system using dual-polarization radar measurements: model improvements and in situ verification

A hydrometeor classification system based on a fuzzy logic technique using dual-polarization radar measurements of precipitation is presented. In this study, five dual-polarization radar measurements (namely horizontal reflectivity, differential reflectivity, specific differential phase, correlation coefficient, and linear depolarization ratio) and altitude relating to environmental melting layer are used as input variables of the system. The hydrometeor classification system chooses one of nine different hydrometeor categories as output. The system presented in this paper is a further development of an existing hydrometeor classification system model developed at Colorado State University (CSU). The hydrometeor classification system is evaluated by comparing inferred results from the CSU CHILL Facility dual-polarization radar measurements with the in situ sample data collected by the T-28 aircraft during the Severe Thunderstorm Electrification and Precipitation Study.     

基于独立分量分析的极化SAR图像非监督分类方法

提出了一种针对极化合成孔径雷达(SAR)图像的新的分类方法--基于独立分量分析(ICA)的非监督分类方法.该方法将ICA和基于模糊集理论的非监督分类方法结合起来.用ICA方法对原始极化SAR图像进行特征提取,并用模糊C均值(FCM)算法对提取出的独立分量图像进行分类.该算法可对极化SAR图像进行自动分类,并减少由相干斑噪声所引起的分类错误,且其收敛速度快、稳定性高.采用SIR-C/X-SAR数据的试验证明了该算法的有效性.     

A numerical study of low-grazing-angle backscatter from ocean-likeimpedance surfaces with the canonical grid method

A numerical study of 14-GHz low-grazing-angle (LGA) backscattering from ocean-like surfaces described by a Pierson-Moskowitz spectrum is presented. Surfaces rough in one dimension are investigated with Monte Carlo simulations performed efficiently through use of the canonical grid expansion in an iterative method of moments. Backscattering cross sections are illustrated at angles from 81° to 89° from normal incidence under the impedance boundary condition (IBC) approximation with the efficiency of the numerical model enabling sufficiently large profiles (8192 λ) to be considered so that angular resolution problems can be avoided. Variations with surface spectrum low-frequency cutoff (ranging over spatial lengths from 175.5 m to 4.29 cm) at 3 m/s wind speed are investigated and initial assessments of the small perturbation method (SPM), composite surface theory, operator expansion method (OEM), small slope approximation (SSA), and curvature corrected SPM predictions are performed. Numerical results show an increase in horizontal (HH) backscatter returns as surface low-frequency content is increased while vertical (VV) returns remain relatively constant, as expected, but none of the approximate models considered are found to produce accurate predictions for the entire range of grazing angles. For the cases considered, HH scattering is always observed to be below VV, further demonstrating the importance of improved hydrodynamical models if “super-event” phenomena are to be modeled     

Measurement of oceanic wind speed from HF sea scatter by skywave radar

Remote measurements of the spatial mean ocean wind speeds were obtained using Doppler spectra resolved to 0.08 Hz from high-resolution HF skywave-radar backscatter measurements of the ocean surface. A standard deviation of 2.4 m/s resulted from the correlation of observed winds over the ocean and the broadening of the Doppler spectra in the vicinity of the higher first-order Bragg line. This broadening, for Doppler spectra unperturbed by the ionospheric propagation, is proportional to the increase in power caused by higher order hydrodynamic and electromagnetic effects in the vicinity of the Bragg line and inversely proportional to the square root of the radio frequency. A lower bound on the measure of wind speed was established at 5 m/s by the low resolution spectral processing and low second-order power. An upper limit is suggested by the steep slope in the region of the sea backscatter spectrum outside the square root of two times the first-order Bragg line Doppler.