Efficient estimation of thermodynamic state incorporating Bayesian model order selection

Aaron D. Lanterman; Matthew L. Cooper; Michael I. Miller

doi:10.1117/12.359939

24 August 1999 Efficient estimation of thermodynamic state incorporating Bayesian model order selection

Aaron D. Lanterman, Matthew L. Cooper, Michael I. Miller

Proceedings Volume 3718, Automatic Target Recognition IX; (1999) https://doi.org/10.1117/12.359939
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

The recognition of targets in infrared scenes is complicated by the wide variety of appearances associated with different thermodynamic states. We represent the variability in the thermodynamic signatures of targets via an expansion in terms of 'eigentanks' derived from a principal component analysis performed over the target's surface. Employing a Poisson sensor likelihood, or equivalently a likelihood based on Csiszar's I-divergence, a natural discrepancy measure for nonnegative images, yields a coupled set of nonlinear equations which must be solved to computed maximum a posteriori estimates of the thermodynamic expansion coefficients. We propose a weighted least-squares approximation to the Poisson loglikelihood for which the MAP estimates are solutions of linear equations. Bayesian model order estimation techniques are employed to choose the number of coefficients; this prevents target models with numerous eigentanks in their representation from having an unfair advantage over simple target models. The Bayesian integral is approximated by Schwarz's application of Laplace's method of integration; this technique is closely related to Rissanen's minimum description length and Wallace's minimum message length criteria. Our implementation of these techniques on Silicon Graphics computers exploits the flexible nature of their rendering engines. The implementation is illustrated in estimating the orientation of a tank and the optimum number of representative eigentanks for real data provided by the U.S. Army Night Vision and Electronic Sensors Directorate.

Citation Download Citation

Aaron D. Lanterman, Matthew L. Cooper, and Michael I. Miller "Efficient estimation of thermodynamic state incorporating Bayesian model order selection", Proc. SPIE 3718, Automatic Target Recognition IX, (24 August 1999); https://doi.org/10.1117/12.359939

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Thermodynamics

Sensors

3D modeling

Principal component analysis

Target recognition

Automatic target recognition

Show All Keywords

Keywords/Phrases

Search In:

Publication Years