Adaptive signal detection with distributed sensors

Nicholas A. Nechval

doi:10.1117/12.327130

17 July 1998 Adaptive signal detection with distributed sensors

Nicholas A. Nechval

Proceedings Volume 3374, Signal Processing, Sensor Fusion, and Target Recognition VII; (1998) https://doi.org/10.1117/12.327130
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

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 R^p, 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.

Citation Download Citation

Nicholas A. Nechval "Adaptive signal detection with distributed sensors", Proc. SPIE 3374, Signal Processing, Sensor Fusion, and Target Recognition VII, (17 July 1998); https://doi.org/10.1117/12.327130

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available