Space Time Adaptive Processing Stap Performance In Non Homogeneous Radar Clutter
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Space Time Adaptive Processing Stap Performance In Non Homogeneous Radar Clutter
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Author :
language : en
Publisher:
Release Date : 2001
Space Time Adaptive Processing Stap Performance In Non Homogeneous Radar Clutter written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with categories.
Abstract report addresses the statistical analysis of the non-homogeneity detector (NHD) for non-Guassian interference scenarios. An important issue in STAP is that of homogeneity of training data. Non-homogeneity of the training data has a deleterious effect on STAP performance in that undernulled clutter significantly degrades detection and false alarm characteristics. Previous work in this area has proposed the use of non-homogeneity detector based on a generalized inner product (GIP). The unsuitability of the GIP based test for non-Guassian interference scenarios is noted. We present a new non-homogeneity detector for non-Guassian interference scenarios which can be modeled by a spherically invariant random process (SIRP). Our work includes a statistical analysis of the NHD for non-Guassian interference taking into account the fact that finite sample support is used for covariance estimation. In particular, exact theoretical expressions for the NHD test statistic PDF and the mean of a related test statistic are derived. We also note that the related test statistic admits a remarkably simple stochastic representation as a ratio of an F-distributed random variable and a beta-distributed loss factor. Based on this development, a formal goodness-of-fit test is presented. Performance analysis is carried out using simulated and measured data from the MCARM Program.
Improving Space Time Adaptive Processing Stap Radar Performance In Nonhomogeneous Clutter
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Author : Ho-hsuan Chang
language : en
Publisher:
Release Date : 1997
Improving Space Time Adaptive Processing Stap Radar Performance In Nonhomogeneous Clutter written by Ho-hsuan Chang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Radar signal detection categories.
Space Time Adaptive Processing For Radar Second Edition
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Author : J.R. Guerci
language : en
Publisher: Artech House
Release Date : 2014-11-01
Space Time Adaptive Processing For Radar Second Edition written by J.R. Guerci and has been published by Artech House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-01 with Technology & Engineering categories.
Space-time adaptive processing (STAP) is an exciting technology for advanced radar systems that allows for significant performance enhancements over conventional approaches. Based on a time-tested course taught in industry, government and academia, this second edition reviews basic STAP concepts and methods, placing emphasis on implementation in real-world systems. It addresses the needs of radar engineers who are seeking to apply effective STAP techniques to their systems, and serves as an excellent reference for non-radar specialists with an interest in the signal processing applications of STAP. Engineers find the analysis tools they need to assess the impact of STAP on a variety of important radar applications. A toolkit of STAP algorithms and implementation techniques allows practitioners the flexibility of adapting the best methods to their application. In addition, this second edition adds brand new coverage on “STAP on Transmit” and “Knowledge-Aided STAP (KA-STAP).
Novel Space Time Adaptive Processing Methods For Gaussian And Non Gaussian Radar Clutter
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Author :
language : en
Publisher:
Release Date : 2001
Novel Space Time Adaptive Processing Methods For Gaussian And Non Gaussian Radar Clutter written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with categories.
Our work on this contract has four main thrusts. We addressed (1) the problem of optimal target detection of a rank one signal in additive non-Gaussian clutter modeled as a spherically invariant random process. Performance analysis of the optimal signal processor was carried out. However, practical implementation of the optimal processor requires know- ledge of the probability density function underlying the clutter, which is often unavailable. Hence, (2) we considered the performance of sub-optimum as well as ad-hoc approximations to the optimal processor. Next, (3) we concerned ourselves with the performance of parametric space-time adaptive processing methods in Gaussian interference and addressed issues of detection probability. constant false alarm rate and reduced training data support. Finally, (4) we provided a rigorous statistical analysis of the recently proposed non-homogeneity detector, which is useful for training data selection in STAP applications.
Knowledge Based Solutions As They Apply To The General Radar Problem
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Author : H. D. Griffiths
language : en
Publisher:
Release Date : 2006
Knowledge Based Solutions As They Apply To The General Radar Problem written by H. D. Griffiths and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.
Knowledge Based Radar Detection Tracking And Classification
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Author : Fulvio Gini
language : en
Publisher: John Wiley & Sons
Release Date : 2008-06-09
Knowledge Based Radar Detection Tracking And Classification written by Fulvio Gini and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-06-09 with Science categories.
Discover the technology for the next generation of radar systems Here is the first book that brings together the key concepts essential for the application of Knowledge Based Systems (KBS) to radar detection, tracking, classification, and scheduling. The book highlights the latest advances in both KBS and radar signal and data processing, presenting a range of perspectives and innovative results that have set the stage for the next generation of adaptive radar systems. The book begins with a chapter introducing the concept of Knowledge Based (KB) radar. The remaining nine chapters focus on current developments and recent applications of KB concepts to specific radar functions. Among the key topics explored are: Fundamentals of relevant KB techniques KB solutions as they apply to the general radar problem KBS applications for the constant false-alarm rate processor KB control for space-time adaptive processing KB techniques applied to existing radar systems Integrated end-to-end radar signals Data processing with overarching KB control All chapters are self-contained, enabling readers to focus on those topics of greatest interest. Each one begins with introductory remarks, moves on to detailed discussions and analysis, and ends with a list of references. Throughout the presentation, the authors offer examples of how KBS works and how it can dramatically improve radar performance and capability. Moreover, the authors forecast the impact of KB technology on future systems, including important civilian, military, and homeland defense applications. With chapters contributed by leading international researchers and pioneers in the field, this text is recommended for both students and professionals in radar and sonar detection, tracking, and classification and radar resource management.
Applications Of Space Time Adaptive Processing
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Author : Richard Klemm
language : en
Publisher: IET
Release Date : 2004-08-13
Applications Of Space Time Adaptive Processing written by Richard Klemm and has been published by IET this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-08-13 with Technology & Engineering categories.
This text discusses various applications of space-time adaptive processing, including applications in OTH-radar, ground target tracking, STAP in real world clutter environments, jammer cancellation, superresolution, active sonar, seismics and communications. It is divided into two parts: the first dealing with the classical adaptive suppression of airborne and spacebased radar clutter, and the second comprising of miscellaneous applications in other fields such as communications, underwater sound and seismics.
Principles Of Space Time Adaptive Processing
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Author : Richard Klemm
language : en
Publisher: IET
Release Date : 2002
Principles Of Space Time Adaptive Processing written by Richard Klemm and has been published by IET this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Technology & Engineering categories.
Annotation This is a systematic introduction to MTI (moving target indication) system design for use in the fields of earth observation, surveillance and reconnaisance, with particular regard to the suppression of clutter returns. Coverage includes signal processing, clutter models, array processinig theory, adapted linear arrays, non-linear antenna configurations, circular arrays, space-frequency techniques, and clutter suppression jamming conditions. This book is a follow up to the author's successful first book on STAP.
Adaptive Radar Detection In The Presence Of Textured And Discrete Interference
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Author : Jeong Hwan Bang
language : en
Publisher:
Release Date : 2013
Adaptive Radar Detection In The Presence Of Textured And Discrete Interference written by Jeong Hwan Bang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Doppler effect categories.
Under a number of practical operating scenarios, traditional moving target indicator (MTI) systems inadequately suppress ground clutter in airborne radar systems. Due to the moving platform, the clutter gains a nonzero relative velocity and spreads the power across Doppler frequencies. This obfuscates slow-moving targets of interest near the "direct current" component of the spectrum. In response, space-time adaptive processing (STAP) techniques have been developed that simultaneously operate in the space and time dimensions for effective clutter cancellation. STAP algorithms commonly operate under the assumption of homogeneous clutter, where the returns are described by complex, white Gaussian distributions. Empirical evidence shows that this assumption is invalid for many radar systems of interest, including high-resolution radar and radars operating at low grazing angles. We are interested in these heterogeneous cases, i.e., cases when the Gaussian model no longer suffices. Hence, the development of reliable STAP algorithms for real systems depends on the accuracy of the heterogeneous clutter models. The clutter of interest in this work includes heterogeneous texture clutter and point clutter. We have developed a cell-based clutter model (CCM) that provides simple, yet faithful means to simulate clutter scenarios for algorithm testing. The scene generated by the CMM can be tuned with two parameters, essentially describing the spikiness of the clutter scene. In one extreme, the texture resembles point clutter, generating strong returns from localized range-azimuth bins. On the other hand, our model can also simulate a flat, homogeneous environment. We prove the importance of model-based STAP techniques, namely knowledge-aided parametric covariance estimation (KAPE), in filtering a gamut of heterogeneous texture scenes. We demonstrate that the efficacy of KAPE does not diminish in the presence of typical spiky clutter. Computational complexities and susceptibility to modeling errors prohibit the use of KAPE in real systems. The computational complexity is a major concern, as the standard KAPE algorithm requires the inversion of an MNxMN matrix for each range bin, where M and N are the number of array elements and the number of pulses of the radar system, respectively. We developed a Gram Schmidt (GS) KAPE method that circumvents the need of a direct inversion and reduces the number of required power estimates. Another unavoidable concern is the performance degradations arising from uncalibrated array errors. This problem is exacerbated in KAPE, as it is a model-based technique; mismatched element amplitudes and phase errors amount to a modeling mismatch. We have developed the power-ridge aligning (PRA) calibration technique, a novel iterative gradient descent algorithm that outperforms current methods. We demonstrate the vast improvements attained using a combination of GS KAPE and PRA over the standard KAPE algorithm under various clutter scenarios in the presence of array errors.
Robust Covariance Matrix Estimation For Radar Space Time Adaptive Processing Stap
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Author : Bosung Kang
language : en
Publisher:
Release Date : 2015
Robust Covariance Matrix Estimation For Radar Space Time Adaptive Processing Stap written by Bosung Kang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Estimating the disturbance or clutter covariance is a centrally important problem in radar space time adaptive processing (STAP) since estimation of the disturbance or interference covariance matrix plays a central role on radar target detection in the presence of clutter, noise and a jammer. The disturbance covariance matrix should be inferred from training sample observations in practice. Traditional maximum likelihood (ML) estimators are effective when homogeneous (target free) training data is abundant but lead to poor estimates, degraded false alarm rates, and detection loss in the regime of limited training. However, large number of homogeneous training samples are generally not available because of difficulty of guaranteeing target free disturbance observation, practical limitations imposed by the spatio-temporal nonstationarity, and system considerations. The problem has been exacerbated by recent advances that have led to more antenna elements (J) and higher temporal resolution (P) time epochs resulting in a large dimension (N = JP).In this dissertation, we look to address the aforementioned challenges by exploiting physically inspired constraints into ML estimation. While adding constraints is beneficial to achieve satisfactory performance in the practical regime of limited training, it leads to a challenging problem. Unlike unconstrained estimators, a vast majority of constrained radar STAP estimators are iterative and expensive numerically, which prohibits practical deployment. We focus on breaking this classical trade-off between computational tractability and desirable performance measures, particularly in training starved regimes. In particular, we exploit both the structure of the disturbance covariance and importantly the knowledge of the clutter rank to yield a new rank constrained maximum likelihood (RCML) estimator of clutter/disturbance covariance. We demonstrate that the rank-constrained estimation problem can in fact be cast in the framework of a tractable convex optimization problem, and derive closed form expressions for the estimated covariance matrix. In addition, we derive a new covariance estimator for STAP that jointly considers a Toeplitz structure and a rank constraint on the clutter component. Past work has shown that in the regime of low training, even handling each constraint individually is hard and techniques often resort to slow numerically based solutions. Our proposed solution leverages the rank constrained ML estimator (RCML) of structured covariances to build a computationally friendly approximation that involves a cascade of two closed form solutions. Performance analysis using the KASSPER data set (where ground truth covariance is made available) shows that the proposed RCML estimator vastly outperforms state-of-the art alternatives even for low training including the notoriously difficult regime of K