Bias estimation using targets of opportunity

Bret D. Kragel; Scott Danford; Shawn M. Herman; Aubrey B. Poore

doi:10.1117/12.738161

21 September 2007 Bias estimation using targets of opportunity

Bret D. Kragel, Scott Danford, Shawn M. Herman, Aubrey B. Poore

Proceedings Volume 6699, Signal and Data Processing of Small Targets 2007; 66991F (2007) https://doi.org/10.1117/12.738161
Event: Optical Engineering + Applications, 2007, San Diego, California, United States

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.

Citation Download Citation

Bret D. Kragel, Scott Danford, Shawn M. Herman, and Aubrey B. Poore "Bias estimation using targets of opportunity", Proc. SPIE 6699, Signal and Data Processing of Small Targets 2007, 66991F (21 September 2007); https://doi.org/10.1117/12.738161

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available