Multi-target Association Between Distributed Passive Sensors Using Tracking Information in 2D Images

Accurate multi-target association between distributed passive sensors is essential for effective multi-target tracking and localization. We propose a multi-target association method between distributed passive sensors using tracking information in 2D images. The key idea is to calculate the statistical values of the perpendicular foot distances of the common vertical line for the line of sight direction vectors of matched point targets among different sensors. And using these values to determine the weight of Kuhn-Munkres (KM) algorithm to find the optimal association results for multi-target between distributed passive sensors. Through numerical simulations, we analyze how angular precision and target density affect both the baseline method and our proposed method. Experiment results show that our method can maintain stable association accuracy while substantially enhancing association performance in scenarios characterized by large sensor measurement errors and high target density.