Multi Sensor Fusion in Distributed Systems

The increasing trend towards connected sensors (“internet of things” and “ubiquitous computing”) derive a demand for powerful distributed estimation methodologies. In tracking applications, the “Distributed Kalman Filter” (DKF) provides an optimal solution under certain conditions. The optimal solution in terms of the estimation accuracy is also achieved by a centralized fusion algorithm which receives either all associated measurements or so-called “tracklets”. However, this scheme needs the result of each update step for the optimal solution whereas the DKF works at arbitrary communication rates since the calculation is completely distributed. Two more recent methodologies are based on the “Accumulated State Densities” (ASD) which augment the states from multiple time instants. In practical applications, tracklet fusion based on the equivalent measurement often achieves reliable results even if full communication is not available. The limitations and robustness of the tracklet fusion will be discussed. On the other hand, different flavors of Covariance Intersection (CI) have the advantage that it is not required to modify the produced tracks from local sites. Theoretical and practical implications of this approach will be presented.

At first, the lecture will explain the origin of the challenges in distributed tracking. Then, possible solutions to them are derived and illuminated. In particular, algorithms will be provided for each presented solution. The list of topics includes: Short introduction to target tracking, Tracklet Fusion, Exact Fusion with cross-covariances, Naive Fusion, Federated Fusion, Decentralized Fusion (Consensus Kalman Filter), Distributed Kalman Filter (DKF), Covariance Intersection, Distributed ASD Fusion, Augmented State Tracklet Fusion.