Signal Detection in Non-Gaussian and Non-white Noise
Author | : Pamela Ann Nielsen |
Publisher | : |
Total Pages | : 360 |
Release | : 1990 |
Genre | : |
ISBN | : |
Download Signal Detection in Non-Gaussian and Non-white Noise Book in PDF, ePub and Kindle
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Signal Detection In Non Gaussian And Non White Noise PDF full book. Access full book title Signal Detection In Non Gaussian And Non White Noise.
Author | : Pamela Ann Nielsen |
Publisher | : |
Total Pages | : 360 |
Release | : 1990 |
Genre | : |
ISBN | : |
Author | : Pankaj Srivastava |
Publisher | : |
Total Pages | : 194 |
Release | : 1991 |
Genre | : |
ISBN | : |
Author | : Saleem A. Kassam |
Publisher | : Springer Science & Business Media |
Total Pages | : 244 |
Release | : 2012-12-06 |
Genre | : Technology & Engineering |
ISBN | : 146123834X |
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 | : Lawrence Lee Feth |
Publisher | : |
Total Pages | : 114 |
Release | : 1969 |
Genre | : |
ISBN | : |
Author | : Gencheng Guo |
Publisher | : |
Total Pages | : |
Release | : 2012 |
Genre | : Signal detection |
ISBN | : |
Author | : Andrew B. Martinez |
Publisher | : |
Total Pages | : 163 |
Release | : 1985 |
Genre | : |
ISBN | : |
Author | : Oscar Chi-Lim Au |
Publisher | : |
Total Pages | : 376 |
Release | : 1991 |
Genre | : |
ISBN | : |
Author | : |
Publisher | : |
Total Pages | : 53 |
Release | : 1992 |
Genre | : |
ISBN | : |
A general analysis of the Ternary Class (M = 2): H sub 0: N vs H sub 1: S1+ N vs H sub 2: S sub 2 + N of signal detection problems is is presented, for completely general signals, i.e., both broadband narrow-band, deterministic or random, in generalized (i.e., non-Gaussian) noise, in the limiting threshold regime. This includes optimum threshold algorithms and system performance, as measured by the appropriate error and detection probabilities. The present treatment, however, is subject to the following constraints: (1) independent noise sampling; (2) ambient noise models, i.e., noise independent of the signals; (3) uniform cost functions, e.g., C sub o (> 0) for errors, and C sub 1 = 0 for correct decisions. Under these conditions, only three principal parameters are needed: delta 12, delta 22 = signal detection parameters (= 'output (S/N) 2') and the correlation coefficient P sub 12 (= P) between the two (threshold) test statistics (or detection 'algorithms') Z sub 1, Z sub 2, apart from the a priori probabilities (q, p sub 1, P sub 2) of the presence of noise alone, S dub 1, and S sub 2. Next steps, to extend the treatment to the general case (M = 3): H sub 1: N + S sub 1, vs H sub 2: S sub 2 + N vs H sub 3 : S sub 3 + N, and to include correlated noise samples, are noted ... Ternary detection, Coherent and incoherent reception, Threshold signal detection, Generalized noise.
Author | : K. J. Sangston |
Publisher | : |
Total Pages | : 25 |
Release | : 1989 |
Genre | : |
ISBN | : |
Recent results of the detection of signals in discrete-time correlated, non-Gaussian noise in which the univariate statistics and a general covariance structure of the noise are known have been obtained. the results are predicted on the assumption that a solution to the signal detection problem based on knowledge of univariate statistics and a convariance structure is 'reasonable, ' even though it is known that in general a non-Gaussian noise process is not completely specified by such information. to examine this issue of 'reasonableness, ' we present two general non-Gaussian noise models that are equivalent in these assumed attributes and yet lead to fundamentally different detection structures. This difference in the detection structures indicate the signal detection problem is not adequately formulated without additional knowledge of the structure of the non-Gaussian noise process. We further present a specific radar example to quantify the difference in the detection structures. (rh).
Author | : Vyacheslav Tuzlukov |
Publisher | : CRC Press |
Total Pages | : 688 |
Release | : 2018-10-08 |
Genre | : Technology & Engineering |
ISBN | : 1420041118 |
Additive and multiplicative noise in the information signal can significantly limit the potential of complex signal processing systems, especially when those systems use signals with complex phase structure. During the last few years this problem has been the focus of much research, and its solution could lead to profound improvements in applications of complex signals and coherent signal processing. Signal Processing Noise sets forth a generalized approach to signal processing in multiplicative and additive noise that represents a remarkable advance in signal processing and detection theory. This approach extends the boundaries of the noise immunity set by classical and modern signal processing theories, and systems constructed on this basis achieve better detection performance than that of systems currently in use. Featuring the results of the author's own research, the book is filled with examples and applications, and each chapter contains an analysis of recent observations obtained by computer modelling and experiments. Tables and illustrations clearly show the superiority of the generalized approach over both classical and modern approaches to signal processing noise. Addressing a fundamental problem in complex signal processing systems, this book offers not only theoretical development, but practical recommendations for raising noise immunity in a wide range of applications.