This paper is concerned with the nonlinear filtering problem for tracking a space object with possibly delayed measurements. In a distributed dynamic sensing environment, due to limited communication bandwidth and long distances between the earth and the satellites, it is possible for sensor reports to be delayed when the tracking filter receives them. Such delays can be complete (the full observation vector is delayed) or partial (part of the observation vector is delayed), and with deterministic or random time lag. We propose an approximate approach to incorporate delayed measurements without reprocessing the old measurements at the tracking filter. We describe the optimal and suboptimal algorithms for filter update with delayed measurements in an orbital trajectory estimation problem without clutter. Then we extend the work to a single object tracking under clutter where probabilistic data association filter (PDAF) is used to replace the recursive linear minimum means square error (LMMSE) filter and delayed measurements with arbitrary lags are be handled without reprocessing the old measurements. Finally, we demonstrate the proposed algorithms in realistic space object tracking scenarios using the NASA General Mission Analysis Tool (GMAT).

Date Published: 7 May 2010
PDF: 12 pages
Proc. SPIE 7691, Space Missions and Technologies, 76910J (7 May 2010); doi: 10.1117/12.849785

Published in SPIE Proceedings Vol. 7691:
Space Missions and Technologies
Joseph Lee Cox; Manfred G. Bester; Wolfgang Fink, Editor(s)