Identification of ground targets from sequential high-range-resolution radar signatures

An approach to identifying targets from sequential high-range-resolution (HRR) radar signatures is presented. In particular, a hidden Markov model (HMM) is employed to characterize the sequential information contained in multiaspect HRR target signatures. Features from each of the HRR waveforms are extracted via the RELAX algorithm. The statistical models used for the HMM states are formulated for application to RELAX features, and the expectation-maximization (EM) training algorithm is augmented appropriately. Example classification results are presented for the ten-target MSTAR data set.     

Automatic target recognition using sequences of high resolutionradar range-profiles

In this paper, we address the problem of joint tracking and recognition of a target using a sequence of high resolution radar (HRR) range-profiles. The likelihood function for the scene configuration combines a dynamics-based prior on the sequence of target orientations with a likelihood for range-profiles given the target orientation. A deterministic model and a conditionally Gaussian model for range-profiles are introduced, and the likelihood functions under each model are compared. Simulations are presented demonstrating recognition of mobile aircraft and ground targets, and results showing performance of the algorithm are given in terms of the expected angular estimation error and the rate of correct recognition     

Wavelet preprocessing for high range resolution radarclassification

We develop a wavelet denoising scheme to aid an automatic target recognition (ATR) system in recognizing aircraft from high range resolution radar (HRR) signatures. A template matching classification technique is used with templates formed from synthetically generated signatures. The goal of the classification system is to achieve classification accuracy equivalent to that obtained with measured HRR signatures. Results suggest that a large portion of HRR signature content is nondiscriminatory. The wavelet denoising process removes the nondiscriminatory information, thereby leading to remarkable increases in classification accuracy. Results are shown for HRR signatures from six aircraft     

Superresolution HRR ATR with high definition vector imaging

A new 1-D template-based automatic target recognition (ATR) algorithm is developed and tested on high range resolution (HRR) profiles formed from synthetic aperture radar (SAR) images of targets taken from the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. In this work, a superresolution technique known as High Definition Vector Imaging (HDVI) is applied to the HRR profiles before the profiles are passed through ATR classification. The new I-D ATR system using HDVI demonstrates significantly improved target recognition compared with previous I-D ATR systems that use conventional image processing techniques. This improvement in target recognition is quantified by improvement in probability of correct classification (PCC). More importantly, the application of HDVI to HRR profiles helps to maintain the same ATR performance with reduced radar resource requirements     

Multiple moving target feature extraction for airborne HRR radar

This study considers the clutter suppression and feature extraction of multiple moving targets for airborne high range resolution (HRR) phased array radar. To avoid the range migration problems that occur in the HRR radar data, we divide each HRR profile into nonoverlapping low range resolution segments. No information is lost due to the division and hence no loss of resolution occurs. We show how to use a vector auto-regressive filtering technique to suppress the clutter. Then a relaxation-based parameter estimation algorithm is presented for multiple moving target feature extraction. Numerical results are given to demonstrate the effectiveness of the algorithm     

Time-varying autoregressive modeling of HRR radar signatures

A time-varying autoregressive (TVAR) model is used for the modeling and classification of high range resolution (HRR) radar signatures. In this approach, the TVAR coefficients are expanded by a low-order discrete Fourier transform (DFT). A least-squares (LS) estimator of the TVAR model parameters is presented, and the maximum likelihood (ML) approach for determining the model order is also presented. The validity of the TVAR modeling approach is demonstrated by comparing with other approaches in estimating time-varying spectra of synthetic signals. The estimated TVAR model parameters are also used as features in classifying HRR radar signatures with a neural network. In the experiment with two sets of noncooperating target identification (NCTI) data, about 93% of samples are correctly classified     

Efficient ATR using compression

We examine various model-based automatic target recognition (MBATR) classifiers to investigate the utility of model-catalog compression realized via signal-vector quantization (VQ) and feature extraction. We specifically investigate the impact of various compression rates and common automatic target recognition (ATR) scenario variations such as noise and occlusion through simulations on high-range resolution (HRR) radar and synthetic aperture radar (SAR) data. For this data, we show that significant computational savings are possible for modest decreases in classification performance.     

Moving target detection via airborne HRR phased array radar

We study moving target detection in the presence of temporally and spatially correlated ground clutter for airborne high range resolution (HRR) phased array radar. We divide the HRR range profiles into large range segments to avoid the range migration problems that occur in the HRR radar data. Since each range segment contains a sequence of HRR range bins, no information is lost due to the division and hence no loss of resolution occurs. We show how to use a vector autoregressive (VAR) filtering technique to suppress the ground clutter. Then a moving target detector based on a generalized likelihood ratio test (GLRT) detection strategy is derived. The detection threshold is determined according to the desired false alarm rate, which is made possible via an asymptotic statistical analysis. After the target Doppler frequency and spatial signature vectors are estimated from the VAR-filtered data as if a target were present, a simple detection variable is computed and compared with the detection threshold to render a decision on the presence of a target. Numerical results are provided to demonstrate the performance of the proposed moving target detection algorithm     

High Range Resolution Profiles as Motion-Invariant Features for Moving Ground Targets Identification in SAR-Based Automatic Target Recognition

A quantitative model analysis is presented to justify the extraction of high range resolution (HRR) profiles from synthetic aperture radar (SAR) images as motion-invariant features for identifying moving ground targets. A comparative study is conducted to assess the effectiveness in the identification process between using HRR profiles and SAR images as target signatures. The results indicate that HRR profiles are just as viable as SAR image for identification. Furthermore, a score-level multi-look fusion identification method has been investigated. It is found that a correct accurate identification rate of greater than 99 percent, a low false alarm rate, and a high level of identification confidence can be achieved, providing very robust performance.     

基于通道校准和HMM的机载雷达健康状态评估

性能退化与间歇故障是机载雷达健康状态的重要反映。针对传统机载雷达健康评估方法缺乏整机级的监测指标、未能综合考虑性能退化和间歇故障，传统BIT电路难以监测间歇故障等工程难题，提出了基于通道校准链路，综合运用通道校准仿真技术、正态云、HMM建立起健康状态评估流程。首先，对通道误差与间歇故障进行分析，得到4类校正系数，建立误差与间歇故障注入仿真流程；然后，基于正态云，利用校正系数的样本方差来表征雷达系统的健康状态，提出统一健康模型，并给出HMM拓扑结构与参数设计方法；最后，建立起基于数据驱动的评估流程并开展应用研究。仿真实验验证了误差和间歇故障仿真流程的可行性、健康状态评估方法的有效性，可获得大于95%的评估准确率；应用案例表明所提方法可行，能解决工程上机载雷达健康状态评估的问题。     

Fractal interpolation of radar signatures for detecting stationarytargets in ground clutter

The fundamentals of fractal geometry are reviewed, and its application to the millimeter-wave radar detection of stationary targets in a clutter background is described. First, high-range-resolution (HRR) profiles are used to determine the fractal interpolation functions needed to create fractal signatures. The fractal dimension is then determined for these signatures. On the basis of the value of the fractal dimension, the signature is declared to represent either a target of interest or clutter. The results of a CFAR (constant false alarm rate) simulation are presented to illustrate the performance of the method. They indicate that the fractal dimension feature used seems to be independent of amplitude. Thus, the fractal dimension information combined with traditional amplitude processing techniques will improve probabilities of detection     

基于小波变换的单脉冲雷达目标RCS测量方法研究

随着现代信号处理技术的发展,对非平稳信号分析和处理的小波分析技术已成功应用于雷达目标特性分析领域,大功率单脉冲雷达作为我国航天测控网的主干设备,具有一定的目标特性识别能力。本文主要针对脉冲雷达RCS测量原理,讨论了基于小波变换的单脉冲雷达空间目标RCS测量方法,提出应发挥窄带低分辨率雷达在未来空间目标识别中的作用。     

中远距空空导弹导引头系统仿真及误差分析

针对中远距空空作战,建立了中远距空空导弹的飞行仿真模型,给出了导弹攻击目标的全过程,并给出了攻击结果,即脱靶量和目标杀伤概率的计算方法。在分析末制导阶段导弹雷达工作过程的基础上,分析了导引头误差对测量目标信息及导弹飞行过程和攻击结果的影响。最后,使用Visual Studio 2010和OpenGL进行了空空导弹的全过程飞行仿真,验证了模型的正确性和有效性。     

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.     

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。     

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.     

SAR ATR performance using a conditionally Gaussian model

A family of conditionally Gaussian signal models for synthetic aperture radar (SAR) imagery is presented, extending a related class of models developed for high resolution radar range profiles. This signal model is robust with respect to the variations of the complex-valued radar signals due to the coherent combination of returns from scatterers as those scatterers move through relative distances on the order of a wavelength of the transmitted signal (target speckle). The target type and the relative orientations of the sensor, target, and ground plane parameterize the conditionally Gaussian model. Based upon this model, algorithms to jointly estimate both the target type and pose are developed. Performance results for both target pose estimation and target recognition are presented for publicly released data from the MSTAR program     

基于遗传算法的一种高分辨率雷达目标识别方法

目标识别是防空信息处理中的一个重要环节,而对空中目标类型的识别还没有成熟的理论。通过对高分辨雷达回波信号的分析,在遗传算法的基础上,提出了一种高分辨雷达目标识别方法,由此进行目标识别。仿真实验结果表明,该方法具有高的识别率和强的抗干扰能力。     

Radar Target Pointing Error Reduction Using Extended Kalman Filtering

A new method of reducing target glint errors in radar systems is presented. The target is modeled as n reflectors whose magnitudes and phases are known. The reflector positions are described by a dynamical model driven by white Gaussian noise. The resulting vibrations of the target reflectors produce glintlike pointing errors in the radar system. An extended Kalman filter is developed to estimate the positions of the target reflectors; this information is used to substantially reduce the pointing error due to glint. Data illustrating this glint reduction is given. The model is extended by the inclusion of clutter effects modeled in the same fashion as the glint phenomenon. The results presented indicate the limits of usefulness of this technique as a function of both receiver noise and relative clutter amplitude.     

Automatic target recognition of time critical moving targets using1D high range resolution (HRR) radar

Synthetic Aperture Radar (SAR) imaging and Automatic Target Recognition (ATR) of moving targets pose a significant challenge due to the inherent difficulty of focusing moving targets. As a result, ATR of moving targets has recently received increased interest. High Range Resolution (HRR) radar mode offers an approach for recognizing moving targets by forming focused HRR profiles with significantly enhanced target-to-(clutter+noise) (T/(C+N)) via Doppler filtering and/or clutter cancellation. A goal of HRR ATR transition is the implementation and evaluation of algorithms exhibiting robustness under extended operating conditions (EOC). The public domain Moving and Stationary Target Acquisition and Recognition (MSTAR) data set was used to study 1D template-based ATR development and performance. Due to the unavailability of a statistically significant moving ground target data set, this approach was taken as an interim step in assessing the separability of ground targets when using range only discriminants. This report summarizes the data and algorithm methodology, simulated performance results, and recommendations