Analysis of Kalman filter based approaches for fusing Out-of-Sequence measurements corrupted by systematic errors

This paper relates to systems for target tracking by fusing measurements from heterogeneous sensors. The measurements are corrupted by systematic errors such as offsets in the value domain and the fusion being complicated due to an out-of-sequence measurements problem. We present an example where the offsets of measurements from two sensors are equally large and have opposite algebraic signs. We show that the timely concentrated fusion of measurements according to a simple buffering approach is better suited for diminishing the unwanted effects of systematic errors than the scattered fusion which results from immediately fusing out-of-sequence measurements by sophisticated out-of-sequence measurement fusing algorithms.