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Proceedings Paper

Precision tracking based on segmentation with optimal layering for imaging sensors
Author(s): Anil Kumar; Yaakov Bar-Shalom; Eliezer Oron
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Paper Abstract

In our previous work we presented a method for precision tracking of a low observable target based on data obtained from imaging sensors. The image was divided into several layers of gray level intensities and thresholded. A binary image was obtained and grouped into clusters using image segmentation techniques. Using the centroid measurements of the clusters, the Probabilistic Data Association Filter (PDAF) was employed for tracking the centroid of the target. In this paper, the division of the image into several layers of gray level intensities is optimized by minimizing the Bayes risk. This optimal layering of the image has the following properties: (a) following the segmentation, a closed-form analytical expression is obtained for the single frame based centroid measurement noise variance; (b) in comparison to the measurement noise variance is smaller by at least a factor of 2, thus improving the performance of the tracker. The simulation results presented validate both the expression for the measurement noise variance as well as the performance predictions of the proposed tracking method.

Paper Details

Date Published: 22 October 1993
PDF: 11 pages
Proc. SPIE 1954, Signal and Data Processing of Small Targets 1993, (22 October 1993); doi: 10.1117/12.157768
Show Author Affiliations
Anil Kumar, Univ. of Connecticut (United States)
Yaakov Bar-Shalom, Univ. of Connecticut (United States)
Eliezer Oron, Israel Aircraft Industries (Israel)


Published in SPIE Proceedings Vol. 1954:
Signal and Data Processing of Small Targets 1993
Oliver E. Drummond, Editor(s)

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