Optimal Data Fusion in Multiple Sensor Detection Systems

TLDR

Multiple sensors are increasingly used for surveillance and communications to enhance reliability, survivability, target detection, and coverage, yet prior work has not addressed data‑fusion algorithm design. This study proposes an optimal data‑fusion architecture for distributed detectors. The architecture weights each detector’s decision by its reliability and applies a threshold to produce the global decision.

Abstract

There is an increasing interest in employing multiple sensors for surveillance and communications. Some of the motivating factors are reliability, survivability, increase in the number of targets under consideration, and increase in required coverage. Tenney and Sandell have recently treated the Bayesian detection problem with distributed sensors. They did not consider the design of data fusion algorithms. We present an optimum data fusion structure given the detectors. Individual decisions are weighted according to the reliability of the detector and then a threshold comparison is performed to obtain the global decision.