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Proceedings Paper

Adaptive signal detection with distributed sensors
Author(s): Nicholas A. Nechval
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Paper Abstract

The problem of distributed detection of a signal in incompletely specified noise is considered. It is assumed that the noise can be modeled as a zero-mean Gaussian random process with unknown slowly varying covariance matrix. A network of m sensors receiving independent and identically distributed observations in Rp, regarding certain binary hypotheses, pass their decisions to a fusion center which then decides which one of the two hypotheses is true. We consider the situation where each sensor employs a generalized maximum likelihood ratio test with its own observations and a threshold, which is the same for all the sensors. This test is invariant to intensity changes in the noise background and achieves a fixed probability of a false alarm. Thus, operating in accordance to the local noise situation, the test is adaptive. In addition, it is shown that the test is UMPI (uniformly most powerful invariant). The fusion center decision is based on k out of m decision rule. The asymptotic (m yields (infinity) ) behavior of k out of m rules for finite k and finite m-k are considered. For these rules, the error probability of making a wrong decision does not tend to zero as m yields (infinity) , unless the probability distributions under the hypotheses satisfy certain conditions.

Paper Details

Date Published: 17 July 1998
PDF: 8 pages
Proc. SPIE 3374, Signal Processing, Sensor Fusion, and Target Recognition VII, (17 July 1998); doi: 10.1117/12.327130
Show Author Affiliations
Nicholas A. Nechval, Aviation Univ. of Riga (Latvia)


Published in SPIE Proceedings Vol. 3374:
Signal Processing, Sensor Fusion, and Target Recognition VII
Ivan Kadar, Editor(s)

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