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Proceedings Paper

Performance evaluation of time-weighted backvalues least squares vs. NOGA track estimators via sensor data fusion and track fusion for small target detection applications
Author(s): Neena Imam; Jacob Barhen; Charles W. Glover
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Paper Abstract

The rapid growth and increasing sophistication of airborne surveillance technology have spurred intense research efforts in the development and implementation of tracking algorithms capable of processing a large number of targets using multi-sensor data. In this paper, a novel tracking algorithm, the NOGA tracker, is presented and compared with the more conventional Time-Weighted Backvalues Least Squares (TWBLS) estimator for accuracy (numerical and phenomenological), ease of implementation, and time performance. The NOGA tracker combines model predictions and sensor measurements to produce best estimates for quantities of interest. The state estimator model used for NOGA is a simple second order auto-regression which is combined with an uncertainty reduction scheme involving nonlinear Lagrange optimization process in which the inverse of a global covariance matrix is used as the natural metric for the Bayesian inference that underlies the combining process. The NOGA tracker explicitly incorporates sensor and model uncertainties in the estimation process, and uses model sensitivities to propagate the associated covariance matrices accurately and in a systematic way.

Paper Details

Date Published: 15 September 2005
PDF: 12 pages
Proc. SPIE 5913, Signal and Data Processing of Small Targets 2005, 59130Z (15 September 2005); doi: 10.1117/12.617690
Show Author Affiliations
Neena Imam, Oak Ridge National Lab. (United States)
Jacob Barhen, Oak Ridge National Lab. (United States)
Charles W. Glover, Oak Ridge National Lab. (United States)

Published in SPIE Proceedings Vol. 5913:
Signal and Data Processing of Small Targets 2005
Oliver E. Drummond, Editor(s)

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