A joint data association, registration, and fusion approach for distributed tracking

In this paper, a joint data association, registration, and fusion method is proposed for distributed tracking. As sensor biases are implicitly hidden behind the local tracks, a pseudo measurement method is used here to allow registration at the track level. A maximum likelihood function is formulated for association, registration and fusion. An expectation maximization (EM) algorithm is then developed to perform the track registration, association, and fusion simultaneously. Computer simulation results demonstrate the proposed method has an improved parameters and state estimation performance. (C) 2015 Elsevier Inc. All rights reserved.