Multiple hypothesis S-dimensional assignment algorithm for data association of angle-only sensors with limited fields of view

This paper focuses on how to obtain globally optimal multisensor data association results under the condition that the number of targets is unknown. This problem is likely to occur in scenarios where the sensors' fields of view (FoVs) are limited. The authors proved that, ideally, the cost of the association result is a convex function of the assumed target number and with the minimal value only if the target number is correctly assumed. Based on this, a multiple hypothesis S-dimensional assignment (MHSDA) algorithm is proposed, which replaces the traditional irrevocable onetime assumption with a multiple hypothesis approach to counteract the effect of target number uncertainty. Furthermore, a hybrid implementation architecture based on Union Find plus MHSDA is proposed to ensure the real-time performance. The proposed algorithm requires neither prior knowledge on the number of targets nor on the FoVs of sensors and is applicable to target dense scenarios. Simulation results under different FoV, target number and measurement noise configurations demonstrate the effectiveness of the proposed algorithm.