a new nearest neighbors data association approach based on fuzzy density clustering

The problem of valid measurement’s associations with true targets called “data association” is an essential challenge in multi-target tracking. previous works often use the nearest neighbor or all neighbor approaches for updating the position of the targets, which are unsuccessful in complex environments and real-time applications, respectively. this paper provides a novel and effective solution to the data association problem in multi-target tracking, offering promising advancements in heavily cluttered environments. the proposed method uses important measurements that are determined based on fuzzy membership degrees. we selected and used valid measurements with a high fuzzy membership degree for updating the position of the targets. in this paper, we used two approaches for the selection of important measurements. the first strategy selects the k measurements with the highest degree of membership among the valid measurements. a second strategy is to give up measurements with very low membership degrees. the ability to solve the data association problem for both approaches under different levels of selecting measurements is evaluated. the proposed method is examined under two scenarios: linear crossing and maneuvering targets. the results show that the proposed technique performs better than fnn, jpdaf, mef-jpdaf, and fuzzy-ga methods based on the rmse criterion .