Confidence measure and performance evaluation for HRRR-based classifiers

Constantino Rago; Tim Zajic; Melvyn Huff; Raman K. Mehra; Ronald P. S. Mahler; Michael J. Noviskey

doi:10.1117/12.477607

31 July 2002 Confidence measure and performance evaluation for HRRR-based classifiers

Constantino Rago, Tim Zajic, Melvyn Huff, Raman K. Mehra, Ronald P. S. Mahler, Michael J. Noviskey

Proceedings Volume 4729, Signal Processing, Sensor Fusion, and Target Recognition XI; (2002) https://doi.org/10.1117/12.477607
Event: AeroSense 2002, 2002, Orlando, FL, United States

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.

Citation Download Citation

Constantino Rago, Tim Zajic, Melvyn Huff, Raman K. Mehra, Ronald P. S. Mahler, and Michael J. Noviskey "Confidence measure and performance evaluation for HRRR-based classifiers", Proc. SPIE 4729, Signal Processing, Sensor Fusion, and Target Recognition XI, (31 July 2002); https://doi.org/10.1117/12.477607

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available