Download or read book Subspace Based Adaptive Detection Using a Generalized Likelihood Ratio Test written by Keith Alan Burgess and published by . This book was released on 1994 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Adaptive Detection in Subspaces written by and published by . This book was released on 1990 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers subspace based adaptive detection in the context of the likelihood ratio test studied by Kelly. The probability of false alarm for this test depends only on the subspace. Thus, we propose choosing the transformation onto the subspace to maximize the probability of detection over a likely class of noise and interference scenarios. An approximate solution to this optimization problem is described. This approach can lead to dramatic increases in the probability of detection given a fixed number of data observations due to a large gain in the statistical stability associated with the reduced dimension subspace. The relationship between subspace design for adaptive detection and partially adaptive beamformer design is explored. Simulations verify the analysis.

Download or read book Adaptive Detection for Multichannel Signals in Non Ideal Environments written by Zeyu Wang and published by CRC Press. This book was released on 2024-06-14 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book systematically presents adaptive multichannel signal detection in three types of non-ideal environments, including sample-starved scenarios, signal mismatch scenarios, and noise plus subspace interference environments. The authors provide definitions of key concepts, detailed derivations of adaptive multichannel signal detectors, and specific examples for each non-ideal environment. In addition, the possible future trend of adaptive detection methods is discussed, as well as two further research points – namely, the adaptive detection algorithms based on information geometry, and the hybrid approaches that combine adaptive detection algorithms with machine learning algorithms. The book will be of interest to researchers, advanced undergraduates, and graduate students in sonar, radar signal processing, and communications engineering.

Download or read book Knowledge Based Generalized Likelihood Ratio Test KB GLRT Exploiting Knowledge of the Clutter Ridge in Airborne Radar written by and published by . This book was released on 2004 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we address knowledge-based radar detection for STAP applications. To this end we exploit, at the design stage, the characteristic structure of the clutter ridge and devise two decision rules according to the Generalized Likelihood Ratio Test (GLRT) and the two-step GLRT criteria. We first focus on the case of a clutter ridge with integer slope and then discuss the more general framework of a non-integer slope parameter. With reference to this last case we only provide approximate GLRT detectors due to the analytical difficulties connected with the exact solution of the problem. The performance analysis shows that the new knowledge-based systems achieve a performance level very close to the optimum detector which assumes the perfect knowledge of the clutter covariance matrix and can outperform some previously proposed adaptive detection schemes.

Download or read book Space time Reduced Rank Methods and CFAR Signal Detection Algorithms with Applications to HPRF Radar written by Tareq F. Ayoub and published by . This book was released on 1998 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: In radar applications, the statistical properties (covariance matrix) of the interference are typically unknown a priori and are estimated from a dataset with limited sample support. Often, the limited sample support leads to numerically ill-conditioned radar detectors. Under such circumstances, classical interference cancellation methods such as sample matrix inversion (SMI) do not perform satisfactorily. In these cases, innovative reduced-rank space-time adaptive processing (STAP) techniques outperform full-rank techniques. The high pulse repetition frequency (HPRF) radar problem is analyzed and it is shown that it is in the class of adaptive radar with limited sample support. Reduced-rank methods are studied for the HPRF radar problem. In particular, the method known as diagonally loaded covariance matrix SMI (L-SMI) is closely investigated. Diagonal loading improves the numerical conditioning of the estimated covariance matrix, and hence, is well suited to be applied in a limited sample support environment. The performance of L-SMI is obtained through a theoretical distribution of the output conditioned signal-to-noise ratio of the space-time array. Reduced-rank techniques are extended to constant false alarm rate (CFAR) detectors based on the generalized likelihood ratio test (GLRT). Two new modified CFAR GLRT detectors are considered and analyzed. The first is a subspace-based GLRT detector where subspace-based transformations are applied to the data prior to detection. A subspace transformation adds statistical stability which tends to improve performance at the expense of an additional SNR loss. The second detector is a modified GLRT detector that incorporates a diagonally loaded covariance matrix. Both detectors show improved performance over the traditional GLRT.

Download or read book Robustness Of The Subspace GLRT To Signal Mismatch written by and published by . This book was released on 1995 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: The robustness of a subspace generalized likelihood ratio test (GLRT) detector to signal mismatch is addressed for data conforming to the generalized multivariate analysis of variance model. This model assumes a deterministic signal of known form in the presence of unknown, colored, Gaussian noise. The subspace GLRT compresses data into a lower dimensional subspace prior to detection. It is shown in the paper that a subspace GLRT reduces the performance loss due to mismatch relative to that of a nonsubspace detector.

Download or read book Convergence Performance of Adaptive Detectors written by and published by . This book was released on 1992 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: Performance results for three multiple-observation binary adaptive detectors are presented. A multiple observation binary adaptive detector consisted of a selected single-observation adaptive detector followed by a binary integrator/detector (J out of M detector). Three types of single observation adaptive detectors are considered: nonconcurrent mean-level adaptive detection (MLAD), concurrent MLAD, and the generalized likelihood ratio test (GLRT). The desired input signal is modeled as a Swerling 11 target. Detection performance PD of each binary adaptive detector is evaluated as a function of the number of input channels N, the number of independent input sample vectors- per-channel used to estimate the unknown input covariance matrix, the order of the binary detector M, the desired probability of false alarm PF, and the matched filter output signal-to-noise (S/N) power ratio. Tables of detection performance are provided that will aid in specifying the optimal J for the J out of M detector and finding the number of input samples-per-channel K* necessary to achieve a 3 dB loss in optimal performance for a given PD, PF, M, N, and single observation detector configuration. Significantly, if was found that K* and J are relatively invarient of the single-observation detector configuration and the chosen PD ... Adaptive filter, Adaptive detector, Adaptive cancellation.

Download or read book Adaptive Radar Detection Model Based Data Driven and Hybrid Approaches written by Angelo Coluccia and published by Artech House. This book was released on 2022-11-30 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows you how to adopt data-driven techniques for the problem of radar detection, both per se and in combination with model-based approaches. In particular, the focus is on space-time adaptive target detection against a background of interference consisting of clutter, possible jammers, and noise. It is a handy, concise reference for many classic (model-based) adaptive radar detection schemes as well as the most popular machine learning techniques (including deep neural networks) and helps you identify suitable data-driven approaches for radar detection and the main related issues. You’ll learn how data-driven tools relate to, and can be coupled or hybridized with, traditional adaptive detection statistics; understand fundamental concepts, schemes, and algorithms from statistical learning, classification, and neural networks domains. The book also walks you through how these concepts and schemes have been adapted for the problem of radar detection in the literature and provides you with a methodological guide for the design, illustrating different possible strategies. You’ll be equipped to develop a unified view, under which you can exploit the new possibilities of the data-driven approach even using simulated data. This book is an excellent resource for Radar professionals and industrial researchers, postgraduate students in electrical engineering and the academic community.

Download or read book Dissertation Abstracts International written by and published by . This book was released on 2008 with total page 906 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book FY Annual ILIR Report written by Naval Undersea Warfare Center (U.S.). In-House Laboratory Independent Research Program and published by . This book was released on 1997 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Spectral Sensing Research For Surface And Air Monitoring In Chemical Biological And Radiological Defense And Security Applications written by Jean-marc Theriault and published by World Scientific. This book was released on 2009-08-11 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides unique perspectives on the state of the art in multispectral/hyperspectral techniques for early-warning monitoring against chemical, biological and radiological (CB&R) contamination of both surface (e.g. land) and air (e.g. atmospheric) environments through the presentation of a comprehensive survey of the novel spectroscopic methodologies and technologies that are emerging to address the CB&R defense and security challenges of the future. The technical content in this book lends itself to the non-traditional requirements for point and stand-off detection that have evolved out of the US joint services programs over many years. In particular, the scientific and technological work presented seeks to enable hyperspectral-based sensing and monitoring that is in real time and in-line; low in cost and labor requirements; and easy to support, maintain and use in military and security-relevant scenarios.

Download or read book Adaptive Detection of Distributed Signals Using Sensor Arrays written by Yuanwei Jin and published by . This book was released on 2003 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Hyperspectral Data Processing written by Chein-I Chang and published by John Wiley & Sons. This book was released on 2013-02-01 with total page 1180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hyperspectral Data Processing: Algorithm Design and Analysis is a culmination of the research conducted in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. Specifically, it treats hyperspectral image processing and hyperspectral signal processing as separate subjects in two different categories. Most materials covered in this book can be used in conjunction with the author’s first book, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, without much overlap. Many results in this book are either new or have not been explored, presented, or published in the public domain. These include various aspects of endmember extraction, unsupervised linear spectral mixture analysis, hyperspectral information compression, hyperspectral signal coding and characterization, as well as applications to conceal target detection, multispectral imaging, and magnetic resonance imaging. Hyperspectral Data Processing contains eight major sections: Part I: provides fundamentals of hyperspectral data processing Part II: offers various algorithm designs for endmember extraction Part III: derives theory for supervised linear spectral mixture analysis Part IV: designs unsupervised methods for hyperspectral image analysis Part V: explores new concepts on hyperspectral information compression Parts VI & VII: develops techniques for hyperspectral signal coding and characterization Part VIII: presents applications in multispectral imaging and magnetic resonance imaging Hyperspectral Data Processing compiles an algorithm compendium with MATLAB codes in an appendix to help readers implement many important algorithms developed in this book and write their own program codes without relying on software packages. Hyperspectral Data Processing is a valuable reference for those who have been involved with hyperspectral imaging and its techniques, as well those who are new to the subject.

Download or read book Fifth ASSP Workshop on Spectrum Estimation and Modeling written by and published by . This book was released on 1990 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book An Adaptive Detection Algorithm written by Edward J. Kelly and published by . This book was released on 1986 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: A general problem of signal detection in a background of unknown Gaussian noise is addressed, using the techniques of statistical hypothesis testing. Signal presence is sought in one data vector, and another independent set of signal-free data vectors is available which share the unknown covariance matrix of the noise in the vector. A likelihood ratio decision rule is derived and its performance evaluated in both the noise-only and signal-plus-noise cases.

Download or read book Coherence written by David Ramírez and published by Springer Nature. This book was released on 2023-01-01 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book organizes principles and methods of signal processing and machine learning into the framework of coherence. The book contains a wealth of classical and modern methods of inference, some reported here for the first time. General results are applied to problems in communications, cognitive radio, passive and active radar and sonar, multi-sensor array processing, spectrum analysis, hyperspectral imaging, subspace clustering, and related. The reader will find new results for model fitting; for dimension reduction in models and ambient spaces; for detection, estimation, and space-time series analysis; for subspace averaging; and for uncertainty quantification. Throughout, the transformation invariances of statistics are clarified, geometries are illuminated, and null distributions are given where tractable. Stochastic representations are emphasized, as these are central to Monte Carlo simulations. The appendices contain a comprehensive account of matrix theory, the SVD, the multivariate normal distribution, and many of the important distributions for coherence statistics. The book begins with a review of classical results in the physical and engineering sciences where coherence plays a fundamental role. Then least squares theory and the theory of minimum mean-squared error estimation are developed, with special attention paid to statistics that may be interpreted as coherence statistics. A chapter on classical hypothesis tests for covariance structure introduces the next three chapters on matched and adaptive subspace detectors. These detectors are derived from likelihood reasoning, but it is their geometries and invariances that qualify them as coherence statistics. A chapter on independence testing in space-time data sets leads to a definition of broadband coherence, and contains novel applications to cognitive radio and the analysis of cyclostationarity. The chapter on subspace averaging reviews basic results and derives an order-fitting rule for determining the dimension of an average subspace. These results are used to enumerate sources of acoustic and electromagnetic radiation and to cluster subspaces into similarity classes. The chapter on performance bounds and uncertainty quantification emphasizes the geometry of the Cramèr-Rao bound and its related information geometry.

Download or read book Conference Record of the Twenty eighth Asilomar Conference on Signals Systems Computers written by Avtar Singh and published by . This book was released on 1994 with total page 916 pages. Available in PDF, EPUB and Kindle. Book excerpt: