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Proceedings Paper

Nonlinear tracking evaluation using absolute and relative metrics
Author(s): Erik P. Blasch; Andy Rice; Chun Yang
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Paper Abstract

Tracking performance is a function of data quality, tracker type, and target maneuverability. Many contemporary tracking methods are useful for various operating conditions. To determine nonlinear tracking performance independent of the scenario, we wish to explore metrics that highlight the tracker capability. With the emerging relative track metrics, as opposed to root-mean-square error (RMS) calculations, we explore the Averaged Normalized Estimation Error Squared (ANESS) and Non Credibility Index (NCI) to determine tracker quality independent of the data. This paper demonstrates the usefulness of relative metrics to determine a model mismatch, or more specifically a bias in the model, using the probabilistic data association filter, the unscented Kalman filter, and the particle filter.

Paper Details

Date Published: 19 May 2006
PDF: 10 pages
Proc. SPIE 6236, Signal and Data Processing of Small Targets 2006, 62360L (19 May 2006); doi: 10.1117/12.666463
Show Author Affiliations
Erik P. Blasch, Air Force Research Lab. (United States)
Andy Rice, Air Force Research Lab. (United States)
Chun Yang, Sigtem Technology, Inc. (United States)

Published in SPIE Proceedings Vol. 6236:
Signal and Data Processing of Small Targets 2006
Oliver E. Drummond, Editor(s)

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