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Proceedings Paper

Adaptive filtering for single target tracking
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Paper Abstract

Many algorithms may be applied to solve the target tracking problem, including the Kalman Filter and different types of nonlinear filters, such as the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Particle Filter (PF). This paper describes an intelligent algorithm that was developed to elegantly select the appropriate filtering technique depending on the problem and the scenario, based upon a sliding window of the Normalized Innovation Squared (NIS). This technique shows promise for the single target, single radar tracking problem domain. Future work is planned to expand the use of this technique to multiple targets and multiple sensors.

Paper Details

Date Published: 11 May 2009
PDF: 10 pages
Proc. SPIE 7336, Signal Processing, Sensor Fusion, and Target Recognition XVIII, 73360C (11 May 2009); doi: 10.1117/12.819451
Show Author Affiliations
Maria Scalzo, Air Force Research Lab. (United States)
Gregory Horvath, Air Force Research Lab. (United States)
Eric Jones, Air Force Research Lab. (United States)
Adnan Bubalo, Air Force Research Lab. (United States)
Mark Alford, Air Force Research Lab. (United States)
Ruixin Niu, Syracuse Univ. (United States)
Pramod K. Varshney, Syracuse Univ. (United States)


Published in SPIE Proceedings Vol. 7336:
Signal Processing, Sensor Fusion, and Target Recognition XVIII
Ivan Kadar, Editor(s)

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