A maximum likelihood approach to joint registration, association and fusion for multi-sensor multi-target tracking

In this paper, we propose a maximum likelihood (ML) approach to address the joint registration, association and fusion problem in multi-sensor and multi-target surveillance. In particular, an expectation maximization (EM) algorithm is employed here. At each iteration of the EM, the extended Kalman filter (EKF) is incorporated into the E-step to obtain the fusion results, while the registration parameters are updated in the M-step. Association of sensor measurements to the targets are also computed as the missing data in the E-step. The main advantage of the proposed method compared to the conventional approaches is that the mutual effects of registration, association and fusion are taken into the consideration when formulating the multi-sensor, multi-target tracking problem. The simulation results demonstrate that the performance of the proposed method in terms of mean square error (MSE) is close to the posterior Cramer-Rao bound (PCRB), and is better than one of the conventional approaches that perform registration, association and fusion separately.