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Proceedings Paper

A robust approach for space based sensor bias estimation in the presence of data association uncertainty
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Paper Abstract

In this paper, an approach to bias estimation in the presence of measurement association uncertainty using common targets of opportunity, is developed. Data association is carried out before the estimation of sensor angle measurement biases. Consequently, the quality of data association is critical to the overall tracking performance. Data association becomes especially challenging if the sensors are passive. Mathematically, the problem can be formulated as a multidimensional optimization problem, where the objective is to maximize the generalized likelihood that the associated measurements correspond to common targets, based on target locations and sensor bias estimates. Applying gating techniques significantly reduces the size of this problem. The association likelihoods are evaluated using an exhaustive search after which an acceptance test is applied to each solution in order to obtain the optimal (correct) solution. We demonstrate the merits of this approach by applying it to a simulated tracking system, which consists of two satellites tracking a ballistic target. We assume the sensors are synchronized, their locations are known, and we estimate their orientation biases together with the unknown target locations.

Paper Details

Date Published: 5 June 2015
PDF: 11 pages
Proc. SPIE 9474, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV, 947407 (5 June 2015); doi: 10.1117/12.2179605
Show Author Affiliations
Djedjiga Belfadel, Univ. of Connecticut (United States)
Richard Osborne, Univ. of Connecticut (United States)
Yaakov Bar-Shalom, Univ. of Connecticut (United States)

Published in SPIE Proceedings Vol. 9474:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV
Ivan Kadar, Editor(s)

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