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Proceedings Paper

Confidence measure and performance evaluation for HRRR-based classifiers
Author(s): Constantino Rago; Tim Zajic; Melvyn Huff; Raman K. Mehra; Ronald P. S. Mahler; Michael J. Noviskey
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Paper Abstract

The work presented here is a continuation of research first reported in Mahler, et. al. Our earlier efforts included integrating the Statistical Features algorithm with a Bayesian nonlinear filter, allowing simultaneous determination of target position, velocity, pose and type via maximum a posteriori estimation. We then considered three alternative classifiers: the first based on a principal component decomposition, the second on a linear discriminant approach, and the third on a wavelet representation. In addition, preliminary results were given with regards to assigning a measure of confidence to the output of the wavelet based classifier. In this paper we continue to address the problem of target classification based on high range resolution radar signatures. In particular, we examine the performance of a variant of the principal component based classifier as the number of principal components is varied. We have chosen to quantify the performance in terms of the Bhattacharyya distance. We also present further results regarding the assignment of confidence values to the output of the wavelet based classifier.

Paper Details

Date Published: 31 July 2002
PDF: 13 pages
Proc. SPIE 4729, Signal Processing, Sensor Fusion, and Target Recognition XI, (31 July 2002); doi: 10.1117/12.477607
Show Author Affiliations
Constantino Rago, Scientific Systems Co., Inc. (United States)
Tim Zajic, Lockheed Martin Tactical Systems (United States)
Melvyn Huff, Scientific Systems Co., Inc. (United States)
Raman K. Mehra, Scientific Systems Co., Inc. (United States)
Ronald P. S. Mahler, Lockheed Martin Tactical Systems (United States)
Michael J. Noviskey, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 4729:
Signal Processing, Sensor Fusion, and Target Recognition XI
Ivan Kadar, Editor(s)

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