On the decorrelated distributed Kalman filter under measurement origin uncertainty

In the recent past, a solution to exact T2TF at arbitrary communication rates was found under the assumption of perfect detection and data assignment. This paper extends this approach by considering the practically relevant problem of measurement origin uncertainty and non-detections. As the local decorrelation of tracks requires the global covariance of the distributed sensor system, two solutions are proposed to cope with false measurements and imperfect detection. The first method approximates the remote track covariances using the modified Riccati equation and the second method uses the fused covariance, fed back from the fusion centre. The proposed methods are evaluated through simulation using multiple sensors with varying sensor parameters, which demonstrates that it is possible to apply a decorrelated distributed Kalman filter in the presence of measurement origin uncertainty.