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Proceedings Paper

On the probability of detection for a 2D sensor array with correlated measurements
Author(s): Ghassan Maalouli
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Paper Abstract

The probability of detection is a key performance metric that assesses a receiver's ability to detect the presence of a signal. Receiver performance is evaluated by comparing empirical measurements against an exact or a bounded theoretical limit. If the detection statistic is based on multiple, independent measurements, it is relatively straight forward to formulate the joint probability density function (PDF) as a multi-variate Gaussian distribution (MVG). In this work, we consider the detection statistic that arises when combining correlated measurements from a two-dimensional array of sensors. The joint PDF does not readily fit into a multi-variate Gaussian model. We illustrate a method by which we can construct a block-diagonal covariance matrix that can be used to cast the joint PDF into the standard MVG form. This expression can then be evaluated numerically to compute a theoretical probability of detection. We validate the authenticity of the joint PDF using Monte-Carlo simulations. We quantify the impact of correlated measurements on the probability of detection.

Paper Details

Date Published: 18 May 2012
PDF: 9 pages
Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 83920R (18 May 2012); doi: 10.1117/12.919795
Show Author Affiliations
Ghassan Maalouli, Raytheon Missile Systems (United States)


Published in SPIE Proceedings Vol. 8392:
Signal Processing, Sensor Fusion, and Target Recognition XXI
Ivan Kadar, Editor(s)

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