Multipassive Sensor Multitarget Efficient Trajectory-Measurement Association Algorithm Based on the Submodular Optimization Theory

Data association is critical for resolving multisensor and multitarget tracking challenge. As the number of sensors and targets grows, the complexity of linking measurements to specific targets also grows. The question of efficiently associating ideal measurements for each target, particularly in multipassive sensor systems, remains unresolved. Inspired by least squares estimation, a cost function is determined that considers angle deviation and distance cost, the original objective function, and a set of constraints. The original objective function is then changed into a submodular function with the property of diminishing marginal benefits after an analysis of the problem's practical importance. Finally, based on the submodular optimization theory, an effective multisensor trajectory-measurement association algorithm is proposed. The simulation results show that using the proposed algorithm, each target only needs to be associated with a small number of high-accuracy measurement azimuths to achieve tracking performance comparable with the previous algorithm.