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     

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     

Critique of some neural network architectures and claims forcontrol and estimation

While there is great potential for successful use of neural network (NN) algorithms in automatic target recognition (ATR) and other pattern identification/classification applications, significant barriers have been encountered that, to date, defy rigorous use of NNs within feedback control designs. The status of several problems and contradictions involving NNs relating to control and estimation theory applications (and to practical failure detection within INS/GPS navigation systems) are summarized here. To give a positive spin and for a balanced perspective, we also mention many novel laudable NN results obtained by invoking the techniques and results of control and estimation theory     

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     

Security applications of computer vision

In an age which bears witness to a proliferation of Closed Circuit Television (CCTV) cameras for security and surveillance monitoring, the use of image processing and computer vision techniques which were provided as top end bespoke solutions can now be realised using desktop PC processing. Commercial Video Motion Detection (VMD) and Intelligent Scene Monitoring (ISM) systems are becoming increasingly sophisticated, aided, in no small way, by a technology transfer from previously exclusively military research sectors. Image processing is traditionally concerned with pre-processing operations such as Fourier filtering, edge detection and morphological operations. Computer vision extends the image processing paradigm to include understanding of scene content, tracking and object classification. Examples of computer vision applications include Automatic Number Plate Recognition (ANPR), people and vehicle tracking, crowd analysis and model based vision. Often image processing and computer vision techniques are developed with highly specific applications in mind and the goal of a more global understanding computer vision system remains, at least for now, outside the bounds of present technology. This paper will review some of the most recent developments in computer vision and image processing for challenging outdoor perimeter security applications. It also describes the efforts of development teams to integrate some of these advanced ideas into coherent prototype development systems     

Performance of 10- and 20-target MSE classifiers

MIT Lincoln Laboratory is responsible for developing the ATR (automatic target recognition) system for the DARPA-sponsored SAIP program; the baseline ATR system recognizes 10 GOB (ground order of battle) targets; the enhanced version of SAIP requires the ATR system to recognize 20 GOB targets. This paper presents ATR performance results for 10- and 20-target mean square error (MSE) classifiers using high-resolution SAR (synthetic aperture radar) imagery.     

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.     

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     

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     

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     

A passive millimeter wave camera for aircraft landing in lowvisibility conditions

Fog and low visibility conditions have hampered aviation since its inception. Fog-related accidents are numerous, and canceled take-offs and landings due to fog and low visibility conditions (Cat III) have significant economic impact on airlines, parcel carriers and general aviation. Millimeter waves have good propagation properties in weather and give adequate spatial resolution when used to image the forward scene. Passive millimeter wave focal plane array cameras are new sensors which, integrated into future guidance and landing systems, promise to be an effective aid, or alternative, to existing technology for aircraft landings and take-offs under Cat III conditions. They can produce visual-like radiometric images at real time frame rates (up to 30 Hz), and are directly amenable to image fusion with infrared and visible images. TRW has been actively involved in developing and productizing this technology both at the hardware and the system levels     

Performance complexity study of several approaches to automatictarget recognition from SAR images

A framework which allows for the direct comparison of alternate approaches to automatic target recognition (ATR) from synthetic aperture radar (SAR) images is described and applied to variants of several ATR algorithms. This framework allows comparisons to be made on an even footing while minimizing the impact of implementation details and accounts for variation in image sizes, in angular resolution, and in the sizes of orientation windows used for training. Alternate approaches to ATR are characterized in terms of the best achievable performance as a function of the complexity of the model parameter database. Several approaches to ATR from SAR images are described and the performance achievable by each for a range of database complexities is studied and compared. These approaches are based on a likelihood test under a conditionally Gaussian model, log-magnitude least squared error, and quarter power least squared error. All approaches are evaluated for a wide range of parameterizations and the dependence on these parameters of both the resulting performance and the resulting database complexity is explored. Databases for all of the approaches are trained using identical sets of images and their performance is assessed under identical testing scenarios in terms of probability of correct classification, confusion matrices, and orientation estimation error. The results indicate that the conditionally Gaussian approach outperforms the other two approaches on average for both target recognition and orientation estimation, that accounting for radar power fluctuation improves performance for all three methods, and that the conditionally Gaussian approach normalized for power delivers average performance that is equal or superior to all other considered approaches     

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。     

飞行力学在图像制导导弹设计中的应用

为满足作战需要 ,改善导弹的总体性能 ,又有利于降低研制成本 ,对图像制导导弹的导引头最大跟踪角速度、极限框架角、搜索方案以及导弹的速度方案 ,进行了分析设计。以此为例 ,对飞行力学在导弹总体与分系统设计中的应用进行了说明。研究结果表明 :飞行力学是协调导弹总体与各分系统之间关系的重要手段 ,在导弹总体和各分系统的设计过程中 ,积极主动地应用飞行力学进行综合设计 ,可获得最佳的总体性能。     

一种多核实时图像处理模块设计与实现

实时图像处理能力是嵌入式系统中影响系统性能的核心因素.为充分挖掘多核DSP性能,从计算机系统结构的角度出发,开展对多核DSP实时图像处理体系结构的研究,设计了一种基于TMS320 C6678的实时图像处理模块.采用FPGA进行实时图像的采集和缓存,利用四线RapidIO高速接口完成FPGA与DSP之间的数据传输,并设计了四级图像缓冲流水处理结构,实现了图像采集、缓存、处理和发送的并行进行.设计了一套基于数据块共享的实时图像处理多核协同处理机制,可以充分利用C6678八个核的优势,实现对图像的实时处理.     

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.     

Improving the productivity of complex electronic systems design byutilizing applied design methodologies

The design process of complex electronic systems consists of four traditional main stages, which are system design, electronic design, mechanical design, and design for manufacturing. Even today when many integrated computer aided design environments are in use for electronic systems design, we still seem to accept that the design process really has to follow this path of four individual design stages. It is common that we are dealing with data transfer problems between different types of CAE-applications. However, there is a possibility to avoid the disadvantages due to integration problems between the design stages if we decide to develop the design methodology itself instead of developing those independent software applications. One effective way to improve the productivity of complex electronic systems design is to tune the so-called systematic design approach by adding special aspects of electronic systems design into the questionnaires used for collecting the requirement lists for further mechanical design and DFM. Another important tool is the carefully made manufacturability analysis. The deep interaction of mechanical and electronic design, extended by manufacturability analysis can also lead to innovative solutions as presented previously by the author. In the article entitled “Manufacturability Analysis-A Useful Subset of Systems Engineering” we were able to show that the number of iteration cycles during the different design and manufacturing stages could be reduced by 50% compared to the conventional design methodology. In this paper, we illustrate additional tools to continue this promising development work     

弹载数字图像处理计算机系统的设计与实现

介绍了红外成像导引系统数字图像处理计算机的硬件设计.它采用了TI公司最新推出的C64xTM系列新一代数字图像信号处理器-TMS320C6414和Xilinx公司超大规模现场可编程逻辑器件Virtex-Ⅱ系列的XC2V2000,实现一个高性能的弹载数字图像处理硬件平台,在红外焦平面成像导引系统中具有较高的实用价值和广泛的应用前景.     

非常规推进发展的现状及前景

综述了几种非常规推进系统的发展现状及技术前景.从美国“空军火箭推进实验室”(AFRPL)的发展工作以及国际电推进会议的论文可以看到,非常规推进的发展早已经不是方案设想,大约在未来的10～15年内,即将在一系列空间任务中付诸实现.同时,更高一个层次的一些非常规推进的新方案,也在开始发展.     

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.