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Proceedings Paper

Multimodel filtering of partially observable space object trajectories
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Paper Abstract

In this paper we present methods for multimodel filtering of space object states based on the theory of finite state time nonhomogeneous cadlag Markov processes and the filtering of partially observable space object trajectories. The state and observation equations of space objects are nonlinear and therefore it is hard to estimate the conditional probability density of the space object trajectory states given EO/IR, radar or other nonlinear observations. Moreover, space object trajectories can suddenly change due to abrupt changes in the parameters affecting a perturbing force or due to unaccounted forces. Such trajectory changes can lead to the loss of existing tracks and may cause collisions with vital operating space objects such as weather or communication satellites. The presented estimation methods will aid in preventing the occurrence of such collisions and provide warnings for collision avoidance.

Paper Details

Date Published: 5 May 2011
PDF: 12 pages
Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80500K (5 May 2011); doi: 10.1117/12.884609
Show Author Affiliations
A. Zatezalo, Scientific Systems Co., Inc. (United States)
A. El-Fallah, Scientific Systems Co., Inc. (United States)
R. Mahler, Lockheed Martin Tactical Defense Systems (United States)
R. K. Mehra, Scientific Systems Co., Inc. (United States)
Khanh D. Pham, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 8050:
Signal Processing, Sensor Fusion, and Target Recognition XX
Ivan Kadar, Editor(s)

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