Recursive feedback adaptive algorithm for decentralized detection system

Most decentralized multisensor detection systems, employing equal probability hypothesis, are unable to keep the optimal detection status when the detection probability is unknown or varying. The problem of optimal detection problem of the decentralized detection system is considered in this paper. Firstly, a recursive state feedback adaptive algorithm is developed when the sensor's false alarm probability and miss alarm probability are unknown and unequal. Based on the online correcting fusion weights, the weights will converge to the optimal values. The convergence and the steady error are then analyzed. The effects of unknown probability and variance variety on the environment of the approach are also analyzed. Finally, simulation results are given to confirm that the performance of the proposed fusion algorithm is satisfactory.