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     

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     

Automatic target recognition for hyperspectral imagery using high-order statistics

Due to recent advances in hyperspectral imaging sensors many subtle unknown signal sources that cannot be resolved by multispectral sensors can be now uncovered for target detection, discrimination, and identification. Because the information about such sources is generally not available, automatic target recognition (ATR) presents a great challenge to hyperspectral image analysts. Many approaches developed for ATR are based on second-order statistics in the past years. This paper investigates ATR techniques using high order statistics. For ATR in hyperspectral imagery, most interesting targets usually occur with low probabilities and small population and they generally cannot be described by second-order statistics. Under such circumstances, using high-order statistics to perform target detection have been shown by experiments in this paper to be more effective than using second order statistics. In order to further address a challenging issue in determining the number of signal sources needed to be detected, a recently developed concept of virtual dimensionality (VD) is used to estimate this number. The experiments demonstrate that using high-order statistics-based techniques in conjunction with the VD to perform ATR are indeed very effective     

Effects of polarization and resolution on SAR ATR

Lincoln Laboratory is investigating the detection and classification of stationary ground targets using high resolution, fully polarimetric, synthetic aperture radar (SAR) imagery. A study is summarized in which data collected by the Lincoln Laboratory 33 GHz SAR were used to perform a comprehensive comparison of automatic target recognition (ATR) performance for several polarization/resolution combinations. The Lincoln Laboratory baseline ATR algorithm suite was used, and was optimized for each polarization/resolution case. Both the HH polarization alone and the optimal combination of HH, HV, and VV were evaluated; the resolutions evaluated were 1 ft/spl times/1 ft and 1 m/spl times/1 m. The data set used for this study contained approximately 74 km/sup 2/ of clutter (56 km/sup 2/ of mixed clutter plus 18 km/sup 2/ of highly cultural clutter) and 136 tactical target images (divided equally between tanks and howitzers).     

Automatic target recognition using enhanced resolution SAR data

Using advanced technology, a new automatic target recognition (ATR) system has been developed that provides significantly improved target recognition performance compared with ATR systems that use conventional synthetic aperture radar (SAR) image-processing techniques. This significant improvement in target recognition performance is achieved by using a new superresolution image-processing technique that enhances SAR image resolution (and image quality) prior to performing target recognition. A computationally efficient two-level implementation of a template-based classifier is used to perform target recognition. The improvement in target recognition performance achieved using superresolution image processing in this new ATR system is quantified     

GMM-based target classification for ground surveillance Doppler radar

An automatic target recognition (ATR) algorithm, based on greedy learning of Gaussian mixture model (GMM) is developed. The GMMs were obtained for a wide range of ground surveillance radar targets such as walking person(s), tracked or wheeled vehicles, animals, and clutter. Maximum-likelihood (ML) and majority-voting decision schemes were applied to these models for target classification. The corresponding classifiers were trained and tested using distinct databases of target echoes, recorded by ground surveillance radar. ML and majority-voting classifiers obtained classification rates of 88% and 96%, correspondingly. Both classifiers outperform trained human operators.     

一种基于红外图像的目标自动识别算法

红外成像制导具有在各种复杂战术环境下自主搜索、捕获、识别和跟踪目标的能力,代表了当代红外制导技术的发展趋势。提出了一种红外图像预处理、跟踪、分类的自动目标识别算法,利用小波变换、形态学方法对红外图像进行预处理,提取不同频带的惯性不变矩作为特征量,利用神经网络进行分类识别,结果表明该算法具有很高的识别率,对于精确制导武器的目标识别研究具有一定的参考价值。     

Image understanding research for automatic target recognition

Automatic target recognition (ATR) is an important capability for defense applications. Many aspects of image understanding (IU) research are traditionally used to solve ATR problems. The authors discuss ATR applications and problems in developing real-world ATR systems and present the status of technology for these systems. They identify several IU problems that need to be resolved in order to enhance the effectiveness of ATR-based weapon systems. They conclude that technological gains in developing robust ATR systems will lead to significant advances in many other areas of applications of image understanding     

MMWR/FLIR/ATR sensor fusion: proof of concept

To improve the relocatable target capabilities of strategic aircraft, a sensor fusion concept using a millimeter-wave radar (MMWR) and a forward-looking infrared (FLIR) system providing inputs to an auto target recognizer (ATR) has been developed. To prove this concept, a cooperative research effort is being conducted by a group of industry leaders in bomber avionics, MMWR, and ATR technologies. The author discusses the concept and the plan developed to test, evaluate, and demonstrate the expected performance     

基于证据理论的模糊信息融合及其在目标识别中的应用

信息融合系统中的不确定性信息常常表现为模糊性和随机性信息。提出了一种在证据理论框架下实现模糊信息融合的方法。该方法首先基于随机集理论刻画模糊信息的隶属函数,获得了模糊观测下具有概然特性的似然函数,该似然函数表示在收集的模糊信息下确定为某一目标的可能性,在数值上表示了传感器信息对某一命题支持的程度,利用似然函数确定传感器输出的基本概率指派,最后利用Dempster-Shafer组合规则实现多传感器信息融合。     

A new approach to automatic target recognition

The fundamental problems of automatic target recognition (ATR) are discussed. A new approach to ATR is suggested that includes: a new method of scoring ATR performance, a new concept of artificial images, a new method called probing for extracting target signature knowledge from image experts, and suggestions for coping with the problem of insufficient test data and algorithm obsolescence     

吸气式空气涡轮冲压发动机的过渡态性能

为计算吸气式空气涡轮冲压(air-turbo-ramjet,ATR)发动机过渡态性能,建立了ATR发动机过渡态模型.通过与传统涡喷发动机供油原则对比得到了ATR发动机供油应遵循的规律,计算得到了给定供油规律下的ATR发动机加减速性能.结果显示ATR发动机在供油规律选择上更加灵活,并能很好地满足喘振裕度的要求.根据ATR发动机自身特点,在补足低转速特性后,本模型可直接模拟ATR发动机起动过程.      

应用压力敏感涂料测量孤立叶片吸力面压力(英文)

以国产温度不敏感的荧光压力敏感涂料和自主建立的测量系统为基础,应用基于光强的压力敏感涂料测量技术获得了叶栅风洞中孤立叶片吸力面的压力分布。实验测量分别在叶栅风洞出口处气流速度0.3和0.4马赫条件下进行,叶片攻角–12°,大气温度与压力分别为15 ℃和95.4kPa。实验叶片吸力面有4排10列测压孔,弯度约40°左右,叶片连同基座被固定在叶栅风洞出口处的下导流档板上,以方便采集荧光图像。实验测量中还同时采用了静压扫描装置进行常规压力测量,以便进行数据对比。在理论与方法确立方面,对基于光强的测量方法进行了回顾并针对自主建立的测量系统提出采用事后校准的方式进行先验校准,以消除校准与测量中参考压力不一致所引起的误差;提出了荧光图像后处理过程的实施步骤及图像对准的操作方法以进一步提高测量的准确性。压力敏感涂料测量所获得的压力值与静压扫描所得数值之间的误差在7%之内,说明实验测量是有效可信的。     

Adaptive boosting for SAR automatic target recognition

The paper proposed a novel automatic target recognition (ATR) system for classification of three types of ground vehicles in the moving and stationary target acquisition and recognition (MSTAR) public release database. First MSTAR image chips are represented as fine and raw feature vectors, where raw features compensate for the target pose estimation error that corrupts fine image features. Then, the chips are classified by using the adaptive boosting (AdaBoost) algorithm with the radial basis function (RBF) network as the base learner. Since the RBF network is a binary classifier, the multiclass problem was decomposed into a set of binary ones through the error-correcting output codes (ECOC) method, specifying a dictionary of code words for the set of three possible classes. AdaBoost combines the classification results of the RBF network for each binary problem into a code word, which is then "decoded" as one of the code words (i.e., ground-vehicle classes) in the specified dictionary. Along with classification, within the AdaBoost framework, we also conduct efficient fusion of the fine and raw image-feature vectors. The results of large-scale experiments demonstrate that our ATR scheme outperforms the state-of-the-art systems reported in the literature     

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     

Automatic spectral target recognition in hyperspectral imagery

Automatic target recognition (ATR) in hyperspectral imagery is a challenging problem due to recent advances of remote sensing instruments which have significantly improved sensor's spectral resolution. As a result, small and subtle targets can be uncovered and extracted from image scenes, which may not be identified by prior knowledge. In particular, when target size is smaller than pixel resolution, target recognition must be carried out at subpixel level. Under such circumstance, traditional spatial-based image processing techniques are generally not applicable and may not perform well if they are applied. The work presented here investigates this issue and develops spectral-based algorithms for automatic spectral target recognition (ASTR) in hyperspectral imagery with no required a priori knowledge, specifically, in reconnaissance and surveillance applications. The proposed ASTR consists of two stage processes, automatic target generation process (ATGP) followed by target classification process (TCP). The ATGP generates a set of targets from image data in an unsupervised manner which will subsequently be classified by the TCP. Depending upon how an initial target is selected in ATGP, two versions of the ASTR can be implemented, referred to as desired target detection and classification algorithm (DTDCA) and automatic target detection and classification algorithm (ATDCA). The former can be used to search for a specific target in unknown scenes while the latter can be used to detect anomalies in blind environments. In order to evaluate their performance, a comparative and quantitative study using real hyperspectral images is conducted for analysis.     

Airborne along-track interferometry for GMTI

Synthetic Aperture Radar (SAR) Along-Track Interferometry (ATI) has been used extensively to measure ocean surface currents. Given its ability to measure small velocities (˜10 cmls) of relatively radar-dark water surfaces, there is great potential that this technique can be adapted for ground moving target indication (GMTI) applications, particularly as a method for detecting very slow targets with small radar cross-sections. Herein, we describe preliminary results from an ATI GMTI experiment. The SAR data described were collected by the dual-frequency NASAIJPL airborne radar in its standard dual-baseline ATI mode. The radar system imaged a variety of control targets including a pick-up truck, sport utility vehicles, passenger cars, a bicycle, and pedestrians over multiple flight passes. The control targets had horizontal velocities of less than 5 m/s. The cross-sections of the targets were not purposely enhanced, although the targets' refiectivities may have been affected by the existence of the GPS equipment used to record the targets' positions. Single-look and multiple-look interferograms processed to the full azimuth resolution were analyzed. In the data processed to date, all of the targets were observed by visual inspection in at least one of the four combinations of dual-frequency, dual-baseline interferometric data. This extremely promising result demonstrates the potential of ATI for GMTI applications.     

吸气式涡轮冲压发动机性能模拟及验证

为验证吸气式涡轮冲压发动机(ATR)模型的精度,参考国外公开的试验数据,对现有ATR模型进行了修正.使用修正后的模型模拟了ATR节流性能和过渡态性能.计算结果和试验数据对比表明:高换算转速条件下的计算结果与试验结果相对误差在1%以内;低换算转速条件下,由于燃气发生器燃气性质和燃烧室出口燃气性质不准确,相对误差有所增加,但未超过5%.全换算转速范围内各参数变化趋势相同.对比结果表明该ATR模型可以很好地模拟ATR慢车以上工况的性能,同时证明了现有ATR模型的可靠性和合理性.      

A prospectus for automatic target recognition

Aspects of autorecognition technology are discussed. The following topics are covered: The context and role of automatic target recognition (ATR), sensors and ATR systems, generic shape discrimination, countermeasures, and current capabilities. The authors conclude that the material supports a very favorable assessment of the power and importance of emerging ATR technology     

Automatic Target Recognition: State of the Art Survey

In this paper a review of the techniques used to solve the automatic target recognition (ATR) problem is given. Emphasis is placed on algorithmic and implementation approaches. ATR algorithms such as target detection, segmentation, feature computation, classification, etc. are evaluated and several new quantitative criteria are presented. Evaluation approaches are discussed and various problems encountered in the evaluation of algorithms are addressed. Strategies used in the data base design are outlined. New techniques such as the use of contextual cues, semantic and structural information, hierarchical reasoning in the classification and incorporation of multisensors in ATR systems are also presented.