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Proceedings Paper

Evaluation of covariance and information performance measures for dynamic object tracking
Author(s): Chun Yang; Erik Blasch; Phil Douville; Lance Kaplan; Di Qiu
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Paper Abstract

In surveillance and reconnaissance applications, dynamic objects are dynamically followed by track filters with sequential measurements. There are two popular implementations of tracking filters: one is the covariance or Kalman filter and the other is the information filter. Evaluation of tracking filters is important in performance optimization not only for tracking filter design but also for resource management. Typically, the information matrix is the inverse of the covariance matrix. The covariance filter-based approaches attempt to minimize the covariance matrix-based scalar indexes whereas the information filter-based methods aim at maximizing the information matrix-based scalar indexes. Such scalar performance measures include the trace, determinant, norms (1-norm, 2-norm, infinite-norm, and Forbenius norm), and eigenstructure of the covariance matrix or the information matrix and their variants. One natural question to ask is if the scalar track filter performance measures applied to the covariance matrix are equivalent to those applied to the information matrix? In this paper we show most of the scalar performance indexes are equivalent yet some are not. As a result, the indexes if used improperly would provide an "optimized" solution but in the wrong sense relative to track accuracy. The simulation indicated that all the seven indexes were successful when applied to the covariance matrix. However, the failed indexes for the information filter include the trace and the four norms (as defined in MATLAB) of the information matrix. Nevertheless, the determinant and the properly selected eigenvalue of the information matrix were successful to select the optimal sensor update configuration. The evaluation analysis of track measures can serve as a guideline to determine the suitability of performance measures for tracking filter design and resource management.

Paper Details

Date Published: 28 April 2010
PDF: 12 pages
Proc. SPIE 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX, 76970B (28 April 2010); doi: 10.1117/12.849855
Show Author Affiliations
Chun Yang, Sigtem Technology, Inc. (United States)
Erik Blasch, Air Force Research Lab. (United States)
Phil Douville, Air Force Research Lab. (United States)
Lance Kaplan, U.S. Army Research Lab. (United States)
Di Qiu, Sigtem Technology, Inc. (United States)


Published in SPIE Proceedings Vol. 7697:
Signal Processing, Sensor Fusion, and Target Recognition XIX
Ivan Kadar, Editor(s)

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