Signal Detection in Non-Gaussian Noise Environments
Author | : Pankaj Srivastava |
Publisher | : |
Total Pages | : 194 |
Release | : 1991 |
Genre | : |
ISBN | : |
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Author | : Pankaj Srivastava |
Publisher | : |
Total Pages | : 194 |
Release | : 1991 |
Genre | : |
ISBN | : |
Author | : Edward J. Wegman |
Publisher | : Springer Science & Business Media |
Total Pages | : 246 |
Release | : 2012-12-06 |
Genre | : Technology & Engineering |
ISBN | : 1461388597 |
Non-Gaussian Signal Processing is a child of a technological push. It is evident that we are moving from an era of simple signal processing with relatively primitive electronic cir cuits to one in which digital processing systems, in a combined hardware-software configura. tion, are quite capable of implementing advanced mathematical and statistical procedures. Moreover, as these processing techniques become more sophisticated and powerful, the sharper resolution of the resulting system brings into question the classic distributional assumptions of Gaussianity for both noise and signal processes. This in turn opens the door to a fundamental reexamination of structure and inference methods for non-Gaussian sto chastic processes together with the application of such processes as models in the context of filtering, estimation, detection and signal extraction. Based on the premise that such a fun damental reexamination was timely, in 1981 the Office of Naval Research initiated a research effort in Non-Gaussian Signal Processing under the Selected Research Opportunities Program.
Author | : Pamela Ann Nielsen |
Publisher | : |
Total Pages | : 360 |
Release | : 1990 |
Genre | : |
ISBN | : |
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 | : 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 | : Steven V. Czarnecki |
Publisher | : |
Total Pages | : 11 |
Release | : 1982 |
Genre | : |
ISBN | : |
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 | : Steven V. Czarnecki |
Publisher | : |
Total Pages | : 217 |
Release | : 1983 |
Genre | : |
ISBN | : |
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 | : 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.