Application of multivariate Gaussian detection theory to known non-Gaussian probability density functions

Craig R. Schwartz; Brian J. Thelen; Arthur C. Kenton

doi:10.1117/12.211374

20 June 1995 Application of multivariate Gaussian detection theory to known non-Gaussian probability density functions

Craig R. Schwartz, Brian J. Thelen, Arthur C. Kenton

Proceedings Volume 2496, Detection Technologies for Mines and Minelike Targets; (1995) https://doi.org/10.1117/12.211374
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The model assumes target detection algorithms and their performance models which are based on data assumed to obey multivariate Gaussian probability distribution functions (PDFs). The applicability of these algorithms and performance models can be generalized to data having non-Gaussian PDFs through the use of transforms which convert non-Gaussian data to Gaussian (or near-Gaussian) data. An example of one such transform is the Box-Cox power law transform. In practice, such a transform can be applied to non-Gaussian data prior to the introduction of a detection algorithm that is formally based on the assumption of multivariate Gaussian data. This paper presents an extension of these techniques to the case where the joint multivariate probability density function of the non-Gaussian input data is known, and where the joint estimate of the multivariate Gaussian statistics, under the Box-Cox transform, is desired. The jointly estimated multivariate Gaussian statistics can then be used to predict the performance of a target detection algorithm which has an associated Gaussian performance model.

Citation Download Citation

Craig R. Schwartz, Brian J. Thelen, and Arthur C. Kenton "Application of multivariate Gaussian detection theory to known non-Gaussian probability density functions", Proc. SPIE 2496, Detection Technologies for Mines and Minelike Targets, (20 June 1995); https://doi.org/10.1117/12.211374

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