A Fusion Algorithm in Asynchronous Tracking Based on Feedback

Owning to different measurement rates, measurements or local estimates arrive at fusion center asynchronously in most multi-sensor tracking systems. Combining track-level fusion and kalman filter, an asynchronous tracking algorithm was proposed in this paper. In the algorithm, local sensor takes the estimate independently on basis of sharing data from fusion center, and fusion center fusion estimates from local sensors by interpolation and carlibration. We provide an extensive set of experimental evidences with a comparative performance analysis with tracking methods representative of the principal approaches. Results show that the method proposed is effective in asynchronous tracking and the accuracy of center is obviously better than local estimates.