A Distributed Detection Fusion Algorithm under Bayesian Criterion and Its Computer Simulation
Hong Li · Jisuanji fangzhen · 2005
In this paper, the signal detection problem is considered when distributed sensors are used and a global decision is desired. Local decisions from the sensors are fed to the data fusion center and the center then yields a global decision based on a fusion rule. The data fusion theories under Bayesian criterion are researched, and the focus is placed on the parallel structure. Fusion rules at the fusion center and the decision rules of sensors are presented. A nonlinear Gauss-Seidel algorithm for the optimal computation of the fusion rules and the decision rules of sensors is proposed. Then, computer simulation for the algorithm is achieved for the fusion cases with two identical sensors, two different sensors and three identical sensors. The results of the computer simulation show that the performance of the fusion system, as compared with the sensor, has been significantly improved. For the case there are three identical sensors in the fusion system, Bayesian risk is reduced by 26.5%, compared with the sensor.