Download or read book A Nonparametric Sequential Pattern Classification Method for Radar Target Recognition written by Young Sun Lim and published by . This book was released on 1993 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Analysis of the Performance of a Parametric and Nonparametric Classification System written by Abdelhamid Djouadi and published by . This book was released on 1987 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation investigates new parametric and nonparametric bounds on the Bayes risk that can be used as a criterion in feature selection and extraction in radar target identification (RTI). For the parametric case, where the form of the underlying statistical distributions is known, Bayesian decision theory offers a well-motivated methodology for the design of parametric classifiers. This investigation provides new bounds on the Bayes risk for both simple and composite classes. Bounds on the Bayes risk for M classes are derived in terms of the risk functions for (M-1) classes, and so on until the result depends only on the Pairwise Bayes risks. When the parameters of the underlying distributions are unknown, an analysis of the effect of finite sample size and dimensionality on these bounds is given for the case of supervised learning. For the case of unsupervised learning, the parameters of these distributions are evaluated by using the maximum likelihood technique by means of an iterative method and an appropriate algorithm. Finally, for the nonparametric case, where the form of the underlying statistical distributions is unknown, a nonparametric technique, the nearest-neighbor (N N) rule, is used to provide estimated bounds on the Bayes risk. Two methods are proposed to produce a finite size risk close to the asymptotic one. The difference between the finite sample size risk and the asymptotic risk is used as the criterion of improvement.

Download or read book Statistical Pattern Recognition for Synthetic Aperture Radar SAR Automatic Target Recognition ATR written by and published by . This book was released on 2001 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: State-of-the-art research on spectral estimation, feature extraction, and pattern recognition algorithms are presented for radar signal processing and automatic target recognition. Advanced space-time spectral estimation algorithms are presented for multiple moving target feature extraction as well as clutter and jamming suppression for airborne high range resolution (HRR) phased-array radar. A nonparametric adaptive filtering-based approach, referred to as the Gapped-data Amplitude and Phase EStimation (GAPES) algorithm, is proposed for the spectral analysis of gapped data sequences as well as synthetic aperture radar (SAR) imaging with angle diversity data fusion. A QUasi-parametric ALgorithm for target feature Extraction (QUALE) algorithm is also investigated for angle diversity data fusion. Support Vector Machines (SVMs) as compared with other advanced classifiers in the MSTAR Public Domain Release and HRR data are found to outperform neural networks and matched filters. A new concept to create negative examples from the known target class is presented and shown to tremendously improve the rejection of confusers. Finally, Information Theoretic Learning (ITL) is proposed as a new algorithm to demix HRR signatures of closely parked targets.

Download or read book Identification of Radar Targets by Pattern Recognition written by William Bernard Goggins and published by . This book was released on 1973 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of identifying radar targets without predetermined knowledge or measurements of their aspect angle is examined. The proposed solution is to use radar magnitude and phase versus frequency data in the resonance region as input data for pattern recognition techniques. Practical methods are developed for measuring radar phase and dealing with polarization effects. Pattern recognition algorithms that optimize separation between pairs of input data are developed. These algorithms are based on digital spatial frequency filtering of curves of the radar return versus the radar frequency. The technique is simulated on a digital computer for three objects - a cylinder, a cone, and a step cylinder - all of about the same size. Results show that at most aspect angles, satisfactory recognition is attained at moderate signal-to-noise ratios.

Download or read book Advanced Pattern Recognition Techniques written by North Atlantic Treaty Organization. Research and Technology Organization and published by . This book was released on 1998 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pattern recognition is the extraction of consistent information from noisy spatiotemporal data. It can be and is currently being used in systems for battlefield supervision, smart weapons, and anti-counterfeiting of all kinds. A currrent application is the automatic detection of land mines and unexploded ordnance. (UXO). The methods employed can be subdivided in the following manner: (1) statistical methods, (2) neuro - methods, (3) fuzzy - methods, and (4) neuro fuzzy methods. Each of these methods has its special advantages and drawbacks, but all of them require the computation of feature variables from measurement or simulation data, e.g. from microwave backscattering. The Lecture series covers the following topics: (1) Introductory overview on pattern recognition techniques, (1) - (4); (2)Feature extraction for pattern recognition by; (a)Electromagnetic, magnetic, and acoustic singularity identification; (b)Model based scattering signatures; (c) Wavelet techniques; (d) SAR/ISAR imaging; (e)Bistatic microwave imaging; and (f)Electromagnetic inversion techniques; (3) Real-time implementation of pattern recognition methods; and (4)Introduction to software and hardware for pattern recognition.

Download or read book Non cooperative Air Target Identification Using Radar written by and published by . This book was released on 1998 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contains the unclassified papers presented at the Symposium. Novel solutions to the Non-Cooperative Target Identification (NCTI) Problem, using radar are proposed. The papers are presented under the following headings: System requirements -- Target characterisation -- Radar measurements and feature extraction -- Target classification -- Scattering techniques, target modelling and validation.

Download or read book Masters Abstracts International written by and published by . This book was released on 1994 with total page 832 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Syntactic Pattern Recognition for Radar Target Identification written by Obed Scott Sands and published by . This book was released on 1986 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: A syntactic pattern recognition system is proposed for use in radar target identification. The system utilizes one of three different level-crossing based pattern representation schemes. Likelihood functions are estimated from relative frequency densities and are used for classification of the symbolic pattern representation. Grammatical inference is used to derive a syntax analysis algorithm from the likelihood function classifier performance is estimated using Monte-Carlo simulations, Directions of further research on this promising topic are discussed.

Download or read book Pattern Classification written by Richard O. Duda and published by John Wiley & Sons. This book was released on 2012-11-09 with total page 680 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first edition, published in 1973, has become a classicreference in the field. Now with the second edition, readers willfind information on key new topics such as neural networks andstatistical pattern recognition, the theory of machine learning,and the theory of invariances. Also included are worked examples,comparisons between different methods, extensive graphics, expandedexercises and computer project topics. An Instructor's Manual presenting detailed solutions to all theproblems in the book is available from the Wiley editorialdepartment.

Download or read book Target Identification Using Radar Imagery and Moment Methods written by George A. Ioannidis and published by . This book was released on 1980 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past, most radar target recognition techniques have concentrated on the use of one-dimensionsal target signatures. However, the development of the Synthetic Aperture Radar (SAR) and the recent development of the Inverse Synthetic Aperture (ISAR) technologies have made it possible to obtain radar images of targets and thus allow the use of two-dimensional pattern classification schemes for automatic radar target recognition. One such two-dimensional shape recognition scheme is a technique known as the method of moments, which has been successfully applied to the recognition of objects imaged in the visible and infrared spectrum. The images used in this investigation were generated by the application of the ISAR technique to turntable radar data and also data obtained from flying aircraft targets.

Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1995 with total page 602 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.

Download or read book Optimal Sequential Waveform Selection for Radar Target Imaging and Classification written by Sameh Mahmoud Sowelam and published by . This book was released on 1997 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Feature Selection Applied to Radar Target Identification written by Ogmundur Snorrason and published by . This book was released on 1987 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Three feature selection algorithms are investigated and applied to characterize optimum sets of frequencies for radar target identification. One algorithm is of the nonparametric discriminant analysis type, the other two algorithms, the pairwise exponential weight distance algorithm and the pairwise probability of error algorithm, are parametric and incorporate information about the measurement noise into the feature selection process. The utility of these feature selection algorithms for radar target identification is then evaluated through Monte-Carlo simulations. It is found that significant gain in classification performance can be achieved by using the optimum sets of frequencies characterized by the parametric algorithms.

Download or read book Deep Learning Network for Classifying Target of Same Shape Using RCS Time Series written by Rashmi Narasimhamurthy and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main intension of this work is to find the warhead and decoy classification and identification. Classification of radar target is one of the utmost imperatives and hardest practical problems in finding out the missile. Detection of target in the pool of decoys and debris is one of the major radas technologies widely used in practice. In this study we mainly focus on the radar target recognition in different shapes like cone, cylinder and sphere based on radar cross section (RCS). RCS is a critical element of the radar signature that is used in this work to identify the target. The concept is to focus on new technique of ML for analyzing the input data and to attain a better accuracy. Machine learning has had a significant impact on the entire industry as a result of its high computational competency for target prediction with precise data analysis. We investigated various machine learning classifiers methods to categorize available radar target data. This chapter summarizes conventional and deep learning technique used for classification of radar target.

Download or read book Automatic Target Recognition written by Matthew J. Wilder and published by . This book was released on 2011 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: Target and pattern recognition systems are in widespread use. Efforts have been made in all areas of pattern recognition to increase the performance of these systems. Feature extraction, feature selection, and classification are the major aspects of a target recognition system. This research proposes algorithms for selecting useful statistical features in pattern/target classification problems in which the features are non-Gaussian distributed. In engineering practice, it is common to either not perform any feature selection procedure or to use a feature selection algorithm that assumes the features are Gaussian distributed. These results can be far from optimal if the features are non-Gaussian distributed, as they often are. This research has the goal of mitigating that problem by creating algorithms that are useful in practice. This work focuses on the performance of three common feature selection algorithms: the Branch and Bound, the Sequential Forward Selection, and Exhaustive Search algorithms. Ordinarily, the performance index used to measure the class separation in feature space involves assuming the data are Gaussian and deriving tractable performance indices that can be calculated without estimating the probability density functions of the class data. The advantage of this approach is that it produces feature selection algorithms that have low computational complexity and do not require knowledge of the data densities. The disadvantage is that these algorithms may not perform reasonably when the data are non-Gaussian. This research examines the use of information-theoretic class separability measures that can deal with the non-Gaussian case. In particular, this work shows that the Hellinger Distance (a type of divergence) has very desirable mathematical properties and can be useful for feature selection when accompanied by a suitable density estimator. The suitable density estimator for this research is the multivariate kernel density estimator. In selecting the best feature subset of non-Gaussian distributed features, results show that the Hellinger distance outperformed the other class separability measures in several instances highlighted in this report.

Download or read book A Two step Procedure for Radar Target Identification Using Correlated Sequential Detection written by Jon A. Zachman and published by . This book was released on 1969 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Recurrent Neural Networks for Radar Target Identification written by and published by . This book was released on 1992 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: A real-time recurrent learning algorithm was applied to a five class radar target identification problem. The wideband radar was assumed to measure both kinematic (tracking information expressed as estimated aspect angles) and high range resolution data from a single, isolated aircraft. The aspect angles (azimuth and elevation) of the aircraft relative to the radar were assumed to be constantly chancing. This created temporal sequences of high range resolution radar signatures that changed as the aspect angles changed. These sequences were used as input features to a recurrent neural network for three radar target identification test cases. The first test case demonstrated the feasibility of using recurrent neural networks for radar target identification. The second test case demonstrated the relationship between sequence length and target recognition accuracy. For the third test case, the recurrent net achieved 96% test set accuracy under the following conditions: 5 aircraft classes, azimuth range between 60 deg and 90 deg, elevation range between +5 deg and -5 deg, 1 deg signature granularity, and signatures corrupted by 5 dBsm scintillation noise ... Neural networks, Recurrent neural networks, Real-time recurrent learning algorithm, Radar target identification, Wideband radar, High range resolution radar, Temporal sequences, Sequence analysis.