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Proceedings Paper

Adaptive test for distributed detection of multidimensional signals
Author(s): Nicholas A. Nechval; Konstantin N. Nechval
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Paper Abstract

In this paper, a general problem of the distributed detection of a constant multidimensional signal with unknown parameters in a background of a zero-mean Gaussian noise with unknown varying covariance matrix is considered. This problem is encountered in many situations of decentralized processing involving a large number of sensors, where noisy processes at these sensors have different covariance matrices. We discuss test statistics at the sensors, where a hypothesis testing results in a sequence of 1 and 0, and at the fusion center, where the k out of m decision rule regarding the presence or the absence of a signal is used. Test statistics at the sensors are obtained by means of a generalized maximum likelihood ratio test. This test is invariant to intensity changes in the noise background and achieves a fixed probability of a false alarm. No learning process is necessary in order to achieve the constant false alarm rate. Operating in accordance to the local noise situation, the test is adaptive. It is shown that this test 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.

Paper Details

Date Published: 4 August 2000
PDF: 9 pages
Proc. SPIE 4052, Signal Processing, Sensor Fusion, and Target Recognition IX, (4 August 2000); doi: 10.1117/12.395085
Show Author Affiliations
Nicholas A. Nechval, Aviation Univ. of Riga (Latvia)
Konstantin N. Nechval, Aviation Univ. of Riga (Latvia)

Published in SPIE Proceedings Vol. 4052:
Signal Processing, Sensor Fusion, and Target Recognition IX
Ivan Kadar, Editor(s)

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