EasiDSlT: A Two-Layer Data Association Method for Multitarget Tracking in Wireless Sensor Networks
Hao Chen, Rui Wang, Li Cui, Lei Zhang · IEEE Transactions on Industrial Electronics · 2014
The technology of multitarget tracking (MTT) has been widely and deeply researched in many fields, such as the radar system and wireless sensor networks (WSNs). However, how to develop a lightweight data association algorithm in a decentralized way is still a challenge, particularly considering the fact that WSNs are resource constrained. This paper presents a two-layer data association method for MTT applications, which are based on low-cost WSNs. To improve the association accuracy of the first layer of the data association, this paper proposes a lightweight reasoning method based on the evidence theory. The example analysis indicates that it can also handle the problem of highly conflicting information fusion. The second layer adopts a Bayesian filtering algorithm. By adoption of the two-layer data association, the computation cost of data association in the MTT technology is balanced in intracluster nodes. Simulation experiments show that the data association algorithm has great performance.