A Pseudo-Measurement Approach to Simultaneous Registration and Track Fusion

In multi-sensor tracking, registration is expected to be performed at the track level instead of the measurement level especially for the distributed sensor networks. However, registration at the track level becomes more difficult due to the implicit sensor biases hidden behind the local tracks. We propose a pseudo-measurement approach to solve the simultaneous registration and fusion problem at the track level. A pseudo-measurement equation is derived from the local trackers, which explicitly reveals the relationship between the pseudo-measurements and the sensor biases in a closed-form expression. The resulting registration model then allows us to formulate the track registration and fusion as a maximum likelihood (ML) estimation problem. We propose using the expectation maximization (EM) approach to perform track registration and fusion simultaneously. Both batch and recursive EM algorithms are developed, accompanied by the performance analysis. Simulation results demonstrate that both EM algorithms are capable of providing accurate estimates. Moreover, we apply the proposed method to an air surveillance radar network which suffers from relatively serious registration problems. The proposed method is verified to effectively fuse and register the tracks generated by local radars and to provide a consistent air picture.