Time filter

Source Type

Li Z.,University of Calgary | Li Z.,Shandong University | Chen S.,University of Calgary | Leung H.,University of Calgary | Bosse E.,Defense R and D Canada DRDC Valcartier
IEEE Transactions on Aerospace and Electronic Systems

In performing surveillance using a sensor network, data association and registration are two essential processes which associate data from different sensors and align them in a common coordinate system. While these two processes are usually addressed separately, they actually affect each other. That is, registration requires correctly associated data, and data with sensor biases will result in wrong association. We present a novel joint sensor association, registration, and fusion approach for multisensor surveillance. In order to perform registration and association together, the expectation-maximization (EM) algorithm is incorporated with the Kalman filter (KF) to give simultaneous state and parameter estimates. Computer simulations are carried out to evaluate the performances of the proposed joint association, registration, and fusion method based on EM-KF. © 2010 IEEE. Source

Discover hidden collaborations