Author: Steven V. Czarnecki
Publisher:
ISBN:
Category :
Languages : en
Pages : 11
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Book Description
The design of a locally optimal detector for a known signal in non-Gaussian noise is discussed. The optimal detector non-linearity is approximated adaptively in the noise pdf tail region, and a polynomial is used to approximate the non-linearity near the mean. Examples for several different noise environments are presented, showing in these cases that the adaptive detector is able to achieve a high level of performance.
Author: I. Kirsteins
Publisher:
ISBN:
Category :
Languages : en
Pages : 59
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Book Description
We present a methodology for the modeling of certain non-stationary and non-gaussian random time series data with application to weak signal detection. Some components of the noise, which give it its nongaussian characteristics, can be individually modeled, synthesized and subtracted to provide a gaussian residual. Further, it is shown that this process can also be carried out when signals are present. The proposed methodology is applied to some Arctic Acoustic data using a combination of adaptive differential quantization and adaptive signal estimation algorithms based on singular-value-decomposition of a data matrix which we have developed. The combination of adaptive differential quantization with low-rank approximations to data matrices or estimated covariance matrices is believed to be a new and effective method for multivariable, robust, adaptive detection.
Author: Saleem A. Kassam
Publisher: Springer Science & Business Media
ISBN: 146123834X
Category : Technology & Engineering
Languages : en
Pages : 244
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Book Description
This book contains a unified treatment of a class of problems of signal detection theory. This is the detection of signals in addi tive noise which is not required to have Gaussian probability den sity functions in its statistical description. For the most part the material developed here can be classified as belonging to the gen eral body of results of parametric theory. Thus the probability density functions of the observations are assumed to be known, at least to within a finite number of unknown parameters in a known functional form. Of course the focus is on noise which is not Gaussian; results for Gaussian noise in the problems treated here become special cases. The contents also form a bridge between the classical results of signal detection in Gaussian noise and those of nonparametric and robust signal detection, which are not con sidered in this book. Three canonical problems of signal detection in additive noise are covered here. These allow between them formulation of a range of specific detection problems arising in applications such as radar and sonar, binary signaling, and pattern recognition and classification. The simplest to state and perhaps the most widely studied of all is the problem of detecting a completely known deterministic signal in noise. Also considered here is the detection random non-deterministic signal in noise. Both of these situa of a tions may arise for observation processes of the low-pass type and also for processes of the band-pass type.
Author: Zeyu Wang
Publisher: CRC Press
ISBN: 1040030424
Category : Technology & Engineering
Languages : en
Pages : 195
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Book Description
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.
Author: Steven V. Czarnecki
Publisher:
ISBN:
Category :
Languages : en
Pages : 217
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Book Description
This dissertation addresses the problem of finding nearly optimal detector structures for non-Gaussian noise environments. It is assumed that the noise statistics are unknown except for a very loose characterization. Under this condition, the goal is to study adaptive detector structures that are simple, yet capable of high levels of performance. Attention is focused on the discrete-time locally optimal detector for a constant signal in independent, identically distributed noise. A definition for non-Gaussian noise is given, several common univariate density models are exhibited, and some physical non-Gaussian noise data is discussed. Two approaches in designing adaptive detector nonlinearities are presented, where it is assumed that the noise statistics are approximately stationary. Both proposals utilized simple measurements of the noise behavior to adapt the detector, and in several examples the adaptive detectors are shown capable of attaining nearly optimal performance levels. A simulation is presented demonstrating their successful application.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 80
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Book Description
The analysis of Parts I and II of the report with this title has been extended into two directions. In the first case, the performance of an adaptive system with respect to signals arriving from directions other than the steering direction is evaluated. It is shown that these signals are reflected more strongly than would be suggested by the sidelobe levels of the adaptive patterns themselves. In the other case, the detection problem is generalized to include the detection of signals known only to lie in a subspace of the space of steering vectors. Again, performance is derived and the penalty associated with the greater uncertainty of the signal model is shown to be small. The analysis of Part I essentially repeated here, both to keep this report self-contained and to present an alternative version of the basic derivations. Keywords: Adaptive antennas; Signal to noise ratio; Maximum likelihood detection statistical hypothesis testing; Signal to noise ratio; Gaussian noise.
Author: Nicholas Biagio Pulsone
Publisher:
ISBN:
Category : Signal detection
Languages : en
Pages : 198
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Book Description
Author: Thomas G. Kincaid
Publisher:
ISBN:
Category :
Languages : en
Pages : 31
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Book Description
The report proposes a design of an adaptive receiver for the detection and estimation of nearly periodic signals in additive Gaussian noise. A nearly periodic signal is defined to be a sample function of a Gaussian random process which can be divided into equal length intervals, called periods, in such a manner that the correlation between periods decreases exponentially with their separation. The receiver computes a low signal-to-noise ratio conditional likelihood ratio from which the observer must make decisions. The likelihood ratio is conditional because the receiver estimates any unknown parameters necessary for the computation of the true likelihood ratio. Thus the receiver can only compute a likelihood ratio conditioned upon these estimates being the true values of the unknown parameters. The receiver consists of pre-emphasis filters followed by a comb filter, energy detector, and weighted summation. A theoretical evaluation of the receiver, in terms of ROC curves, is made for the special case of nearly periodic signals with statistically independent equal-strength harmonics in white noise of known power. (Author).
Author: Edward J. Kelly
Publisher:
ISBN:
Category :
Languages : en
Pages : 14
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Book Description
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.
Author: Melvin M. Weiner
Publisher: CRC Press
ISBN: 1420026496
Category : Technology & Engineering
Languages : en
Pages : 1240
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Book Description
In our modern age of remote sensing, wireless communication, and the nearly endless list of other antenna-based applications, complex problems require increasingly sophisticated solutions. Conventional antenna systems are no longer suited to high-noise or low-signal applications such as intrusion detection. Detailing highly effective approaches to non-Gaussian weak signal detection, Adaptive Antennas and Receivers provides an authoritative introduction to state-of-the-art research on the modeling, testing, and application of these technologies. Edited by innovative researcher and eminent expert Melvin M. Weiner, this book is the first to integrate three advanced approaches to non-Gaussian weak signal detection into a single reference: homogeneous partitioning of the surveillance volume, adaptive antennas, and adaptive receivers. Comprising self-contained chapters contributed by renowned experts such as Donald D. Weiner and Ronald Fante, each chapter explores the techniques, theoretical basis, and applications of the approach under discussion. The book considers signal detection in the presence of external noise such as clutter residue, interference, atmospheric noise, jammers, external thermal noise, in vivo surrounding tissue, and camouflaging material, making it ideal for use across a broad spectrum of applications. This authoritative reference supplies more than 750 figures and tables, 1460 equations, and 640 references. Adaptive Antennas and Receivers is an ideal resource for improving performance in surveillance, communication, navigation, artificial intelligence, computer tomography, neuroscience, and intrusion detection systems, to name only a few.
