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Proceedings Paper

Bias estimation using targets of opportunity
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Paper Abstract

Fusion of data from multiple sensors can be hindered by systematic errors known as biases. Specifically, the presence of biases can lead to data misassociation and redundant tracks. Fortunately, if an estimate of the unknown biases can be obtained, the measurements and transformations for each sensor can be debiased prior to fusion. In this paper, we present an algorithm that uses targets of opportunity in the sensor field-of-view for online estimation of time-variant biases. The algorithm uses the singular value decomposition (SVD) to automatically handle the issue of parameter observability during tracking, allowing for shorter estimation windows and more accurate bias estimation. Our approach extends the novel methods proposed in the companion paper by Herman and Poore that used the SVD within a nonlinear least-squares estimator to handle the issue of parameter observability during offine estimation of time-invariant biases using truth data.

Paper Details

Date Published: 21 September 2007
PDF: 16 pages
Proc. SPIE 6699, Signal and Data Processing of Small Targets 2007, 66991F (21 September 2007); doi: 10.1117/12.738161
Show Author Affiliations
Bret D. Kragel, Numerica Corp. (United States)
Scott Danford, Numerica Corp. (United States)
Shawn M. Herman, Numerica Corp. (United States)
Aubrey B. Poore, Numerica Corp. (United States)


Published in SPIE Proceedings Vol. 6699:
Signal and Data Processing of Small Targets 2007
Oliver E. Drummond; Richard D. Teichgraeber, Editor(s)

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