Near optimal two-tier target tracking in sensor networks

A distributed two-tier near optimal algorithm is proposed for target tracking in sensor networks. Tier one is a multiple hypothesis tracking (MHT) algorithm where the Viterbi algorithm is used. In this tier, only binary data is used to obtain a rough region around the target. Tier two improves the accuracy of the MHT decision by localized maximum likelihood. This reduces the computational complexity and the communication costs between sensors over the global maximum likelihood approach. It also results in higher sensor power efficiency, hence longer service time of the tracking network. This two-tier system is a distributed near optimal tracking algorithm. The localized maximum likelihood tracking can tolerate errors made by the Viterbi algorithm in tier one, hence the overall algorithm is robust.