Correlation filters for aircraft identification from radar rangeprofiles

The potential for identifying aircraft using one or more radar range profiles, in conjunction with a correlator, is investigated. Two types of filter which maximize the expected value of certain correlation peaks are described. The effectiveness of one type of filter was investigated in identification experiments using an extensive data set of real radar range profiles of 24 different aircraft. The results suggest that reliable identification is possible provided aircraft aspect information is used and identifications are based on multiple profiles     

Performance analysis of a radar target discrimination technique

The performance of a radar target discrimination technique using multiple-frequency scattering amplitude without phase data is investigated. Based on the concept of natural resonance frequencies, the technique is aspect independent so that a priori information of the aspect angle is not necessary. The radar cross sections (RCSs) of spheroids are calculated numerically to simulate the received radar returns for distinguishing different spheroids and wires in the resonance frequency range by the proposed technique. By Monte Carlo simulation, the discrimination error rate is estimated as a function of the standard deviation of added noise. The numerical results show that the discrimination algorithm works well under moderately noisy situations and can be applied even in a high-resonance frequency range     

Radar target classification of commercial aircraft

With the increased availability of coherent wideband radars there has been a renewed interest in radar target recognition. A large bandwidth gives high resolution in range which means target discrimination may be possible. Coherence makes cross-range resolution and radar images possible. Some of the problems of classifying high resolution range profiles (HRRPs) are examined and simple preprocessing techniques which may aid subsequent target classification are investigated. These techniques are applied to HRRP data acquired at a local airport using the Microwave Radar Division (MRD) mobile radar facility It is found that Boeing 727 and Boeing 737 aircraft can be reliably distinguished over a range of aspect angles. This augers well for future target classification studies using HRRPs     

Radar target identification using the bispectrum: a comparativestudy

Radar target identification is performed using time-domain bispectral features. The classification performance is compared with the performance of other classifiers that use either the impulse response or frequency domain response of the unknown target. The classification algorithms developed here are based on the spectral or the bispectral energy of the received backscatter signal. Classification results are obtained using simulated radar returns derived from measured scattering data from real radar targets. The performance of classifiers in the presence of additive Gaussian (colored or white), exponential noise, and Weibull noise are considered, along with cases where the azimuth position of the target is unknown. Finally, the effect on classification performance of responses horn extraneous point scatterers is investigated     

Probability of Detecting Aircraft Targets

The statistics of aircraft radar cross section (RCS) are estimated by fitting members of the chi-square family of distributions to empirical distributions obtained from blocks of RCS data, each block of data corresponding to a particular aircraft azimuth aspect. The parameters of the fitted distributions vary with azimuth aspect angle, type of aircraft, and radar frequency. Detection probabilities based on the estimated statistics are calculated and compared with detection probabilities based on Rayleigh statistics. This comparison indicates that the average value of radar cross section has much more effect on probability of detection than the normalized variance of RCS, and in the usual situation tends to mask the effect of the normalized variance on probability of detection.     

Classification of radar targets using synthetic neural networks

Radar target classification performance of neural networks is evaluated. Time-domain and frequency-domain target features are considered. The sensitivity of the neural network algorithm to changes in network topology and training noise level is examined. The problem of classifying radar targets at unknown aspect angles is considered. The performance of the neural network algorithms is compared with that of decision-theoretic classifiers. Neural networks can be effectively used as radar target classification algorithms with an expected performance within 10 dB (worst case) of the optimum classifier     

Radar target classification using doppler signatures of human locomotion models

The problem of target classification for ground surveillance Doppler radars is addressed. Two sources of knowledge are presented and incorporated within the classification algorithms: 1) statistical knowledge on radar target echo features, and 2) physical knowledge, represented via the locomotion models for different targets. The statistical knowledge is represented by distribution models whose parameters are estimated using a collected database. The physical knowledge is represented by target locomotion and radar measurements models. Various concepts to incorporate these sources of knowledge are presented. These concepts are tested using real data of radar echo records, which include three target classes: one person, two persons and vehicle. A combined approach, which implements both statistical and physical prior knowledge provides the best classification performance, and it achieves a classification rate of 99% in the three-class problem in high signal-to-noise conditions.     

Improved target classification using optimum polarimetric SARsignatures

We present a new method for automatic target/object classification by using the optimum polarimetric radar signatures of the targets/objects of interest. The state-of-the-art in radar target recognition is based mostly either on the use of single polarimetric pairs or on the four preset pairs of orthogonal polarimetric signatures. Due to these limitations, polarimetric radar processing has been fruitful only in the area of noise suppression and target detection. The use of target separability criteria for the optimal selection of radar signal state of polarizations is addressed here. The polarization scattering matrix is used for the derivation of target signatures at arbitrary transmit and receive polarization states (arbitrary polarization inclination angles and ellipticity angles). Then, an optimization criterion that minimizes the within-class distance and maximizes the between-class metrics is used for the derivation of optimum sets of polarimetric states. The results of the application of this approach on real synthetic aperture radar (SAR) data of military vehicles are obtained. The results show that noticeable improvements in target separability and consequently target classification can be achieved by the use of the optimum over nonoptimum signatures     

High Range-Resolution Monopulse Tracking Radar

High range-resolution monopulse (HRRM) tracking radar which maintains wide instantaneous bandwidth through both range and angle error sensing channels provides range, azimuth, elevation, and amplitude for each resolved part of the target. The three-dimensional target detail can be used to improve and extend radar performance in several ways: for improved precision of target location, for target classification and recognition, to counter repeater-type ECM, to improve low-angle multipath tracking, to resolve multiple targets, as a miss-distance measurement capability, and for improved tracking in chaff and clutter. These have been demonstrated qualitatively except for the ECCM to repeater ECM and low-altitude tracking improvement. Initial results from an experimental HRRM radar with 3-ns pulse length show resolution of aircraft into its major parts and precise location of each resolved part accurately in range and angle. Realtime closed-loop tracking is performed on aircraft in flight using high-speed sampled, digitized, and processed HRRM range and angle video data. Clutter rejection capability is also demonstrated.     

History of automotive anticollision radars and final experimentalresults of a MM-Wave car radar developed by the Technical University ofMunich

The history of development of automotive radars in different countries since 1972 is described using a short comparison of radar types. The authors indicate the planning of introduction of car radars in the near future according to information supplied by car companies. The purpose of the development of an automotive radar was to test different signal processing procedures both for distance and Doppler evaluation and also for a digital wavefront reconstruction to find out the angle position of a target. The block diagram, the main properties, the technical data of the radar system, the used antennas and the multiplexing of transmitting antennas is described. Finally some experimental results have been obtained under real traffic conditions     

非合作目标动态RCS仿真方法

针对非合作目标难以开展动态测量的问题,根据空气动力学原理提出了一种非合作目标动态雷达散射截面（RCS）仿真方法。该方法首先建立测量背景下典型飞行航路模型,然后计算雷达视线在机体坐标系上的时变姿态角。根据姿态角开展电磁计算,获得F-117A隐身攻击机在侧站平飞、背站拉起、对站俯冲、侧站盘旋4种航路下的动态RCS数据。着重分析了动、静态RCS特性在起伏目标检测性能评估上的差异。结果表明：静态RCS特性难以反映目标运动时真实的雷达特性,利用静态数据描述目标特性可能导致错误结论,而文中方法获取的动态RCS数据可以提高结论的完整性和可信度。     

Target Identification by Natural Resonance Estimation

This paper presents a target identification method based on an estimation of the natural frequencies of oscillation in transient radar signatures. The emphasis is placed upon signal modeling and estimation ation strategy rather than relating resonance locations to physical structures. Salient features of this identification method are: 1) target aspect angle is not needed, 2) multiple targets of the same type can be illuminated simultaneously, and 3) bandpass interrogation ion pulses can be used. The latter feature is compatible with existing radar facilities. The method is applied to some simulated transfer functions, and factors affecting estimate accuracy are discussed.     

一种适用于现代飞机火控系统的空-空导弹发射火控计算数学模型

给出的火控计算模型由两部分构成：首先利用机载雷达和其它传感器提供的信息,求解目标速度Vt和进入角Q,然后采用表格函数线性插值与解析修正相结合的方法,来求解导弹发射的最大、最小允许距离。最后给出了仿真结果。该数学模型已经用于某现役歼击机改装的雷达火控系统。     

Space-borne technology and inversion techniques for radar

This presents recent progress of the state-of-the-art of space-borne radar technology in case of either earth-orbiting or planetary-orbiting satellites and space probes, respectively. In addition to that, recent progress is also discussed concerning specific inversion techniques to evaluate radar measurements; i.e., the art of deriving the relevant physical quantities to be determined such as terrain and depth profiles for planetary surface and/or subsurface structures ((3D)-profiles) from radar data measured depending on signal frequency, aspect angle, polarization, etc., as a function of time.     

HRR Detector for Slow-Moving Targets in Sea Clutter

The radar detection of targets in the presence of sea clutter has historically relied upon the radial velocity of targets with respect to the radar platform either by exploiting the relative target Dopplers (for targets with sufficient radial velocity) or by discerning the paths targets traverse from scan to scan. For targets with little to no radial velocity component, though, it can become quite difficult to differentiate targets from the surrounding sea clutter. This paper addresses the detection of slow-moving targets in sea clutter using a high resolution radar (HRR) such that the target has perceptible extent in range. Under the assumption of completely random sea clutter spikes based on an epsiv-contaminated mixture model with the signal and clutter powers known, optimal detection performance results from using the likelihood ratio test (LRT). However, for realistic sea clutter, the clutter spikes tend to be a localized phenomenon. Based upon observations from real radar data measurements, a heuristic approach exploiting a salient aspect of the idealized LRT is developed which is shown to perform well when applied to real measured sea clutter.     

基于部件分解法的运载火箭RCS仿真计算方法

为估算运载火箭的RCS（Radar Cross Section,雷达散射截面积）,采用部件分解法对运载火箭进行电磁散射几何建模,根据飞行过程中运载火箭和雷达的几何关系建立雷达照射目标视线角的计算模型,并运用高频散射理论提出运载火箭RCS的仿真计算方法;最后,对运载火箭的静态RCS和动态RCS进行仿真计算与分析.结果表明：对运载火箭电磁散射几何建模合理可行,提出的火箭RCS计算方法可以满足工程应用需要.采用该方法仅修改几何建模中的模型结构和部分尺寸参数即可方便计算不同型号运载火箭的RCS特性,可以为航天测控雷达系统设计和布站优化提供依据.     

Signal processing of wide bandwidth and wide beamwidth P-3 SAR data

This research is concerned with multidimensional signal processing and image formation with FOliage PENetrating (FOPEN) airborne radar data which were collected by a Navy P-3 ultra wideband (UWB) radar in 1995. The digital signal processors that were developed for the P-3 data commonly used a radar beamwidth angle that was limited to 35 deg. Provided that the P-3 radar beamwidth angle (after slow-time FIR filtering and 6:1 decimation) was 35 deg, the P-3 signal aperture radar (SAR) system would approximately yield alias-free data in the slow-time Doppler domain. We provide an analysis here of the slow-time Doppler properties of the P-3 SAR system. This study indicates that the P-3 database possesses a 50 deg beamwidth angle within the entire [215, 730] MHz band of the P-3 radar. We show that the 50-degree beamwidth limit is imposed by the radar (radial) range swath gate; a larger beamwidth measurements would be possible with a larger range swath gate. The 50-degree beamwidth of the P-3 system results in slow-time Doppler aliasing within the frequency band of [444, 730] MHz. We outline a slow-time processing of the P-3 data to minimize the Doppler aliasing. The images which are formed via this method are shown to be superior in quality to the images which are formed via the conventional P-3 processor. In the presentation, we also introduce a method for converting the P-3 deramped (range-compressed) data into its alias-free baseband echoed data; the utility of this conversion for suppressing radio frequency interference signals is shown     

基于复近似消息传递的前视雷达成像分辨率增强算法

当机载/弹载雷达工作在前视状态时,由于成像场景内不同角度处目标的多普勒差异很小,很难得到 高的角度分辨率。针对海面舰船目标的前视成像应用,利用成像区域具有明显稀疏性的特点,提出一种基于复 近似消息传递压缩感知处理的前视成像角度分辨率增强算法,建立前视成像的线性观测信号模型,给出复近似 消息传递的迭代计算过程,以及多通道雷达前视成像的处理流程。通过仿真数据和 X 波段雷达实测数据的处 理结果验证了该方法的有效性。     

High resolution 3D “snapshot” ISAR imaging and featureextraction

We have developed a new formulation for three dimensional (3D) radar imaging of inverse synthetic aperture radar (ISAR) data based on recent developments in high resolution spectral estimation theory. Typically for non real-time applications, image formation is a two step process consisting of motion determination and image generation. The technique presented focuses on this latter process, and assumes the motion of the target is known. The new technique offers several advantages over conventional techniques which are based on the correlation imaging function. In particular, the technique provides for a direct 3D estimate (versus back projection to a 3D target grid matrix) of the locations of the dominant scattering centers using only a minimum set of independent 2D range-Doppler ISAR “snapshots” of the target. Because of the snapshot nature of the technique, it is particularly applicable to 3D imaging of sectors of sparse-angle data, for which the sidelobes of the correlation imaging integral become high. Furthermore, the technique provides for an estimate of amplitude and phase of each scattering center as a function of aspect angle to the target, for those aspect angles which encompass the set of 2D range-Doppler snapshots. Results illustrating the technique developed are presented for both simulated and static range data     

Bayesian gamma mixture model approach to radar target recognition

This paper develops a Bayesian gamma mixture model approach to automatic target recognition (ATR). The specific problem considered is the classification of radar range profiles (RRPs) of military ships. However, the approach developed is relevant to the generic discrimination problem. We model the radar returns (data measurements) from each target as a gamma mixture distribution. Several different motivations for the use of mixture models are put forward, with gamma components being chosen through a physical consideration of radar returns. Bayesian formalism is adopted and we obtain posterior distributions for the parameters of our mixture models. The distributions obtained are too complicated for direct analytical use in a classifier, so Markov chain Monte Carlo (MCMC) techniques are used to provide samples from the distributions. The classification results on the ship data compare favorably with those obtained from two previously published techniques, namely a self-organizing map and a maximum likelihood gamma mixture model classifier.