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Proceedings Paper

UMPI test for adaptive signal detection
Author(s): Nicholas A. Nechval
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Paper Abstract

The problem of detection of a signal in noise analyzed in the literature assumes a complete statistical knowledge of the received signal. However, in radar, sonar and other detection problems, the signal is embedded in a noise whose characteristics are not completely known and are changing with time. In such situations, the test statistics must be based on some invariant characteristics ofthe noise density function rather than on some specific form of noise density function. In this paper, a general problem of signal detection in a background of unknown Gaussian noise is addressed. Such a noise density function approximates physical noise encountered in different situations. Using the techniques of statistical hypothesis testing, a generalized maximum likelihood ratio (GMLR) test is derived. This test is invariant to intensity changes in the noise background and achieves a fixed probability of a false alarm. Thus, operating in accorthnce to the local noise situation, the test is adaptive. It is shown that the test obtained is uniformly most powerful invariant (UMPI) and robust against departures from normality in the following sense. It is still UMPI in a broad class of distributions, and the null distribution under any member of the class is the same as that under normality. Keywords : noise, broad class of distributions, adaptive test, signal detection

Paper Details

Date Published: 28 July 1997
PDF: 12 pages
Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997); doi: 10.1117/12.280841
Show Author Affiliations
Nicholas A. Nechval, Aviation Univ. of Riga (Latvia)


Published in SPIE Proceedings Vol. 3068:
Signal Processing, Sensor Fusion, and Target Recognition VI
Ivan Kadar, Editor(s)

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