A discrepant information selection mechanism for cooperative sensors

Abstract

Aiming at the discrepancy of information raised by different fields of view of cooperative sensors, this paper proposes a fusion rule based on a new information-theoretic cost function. Specifically, the optimal function is proposed based on the principle of Minimum Discrimination Information (MDI), where the fusion weights are the functions of the label set. The solution of the optimal function exhibits the information selection mechanism, i.e., the GLMB components of the global multitarget density are selected among the local GLMB components based on their weights. Besides, to evaluate the performance of the proposed method, we also propose the overlapping indicator which assesses the similarity among the FoVs of cooperative sensors. The simulation results demonstrate that the proposed method achieves robust performance under different values of the overlapping indicator.