Discrimination gain to optimize detection and classification

Keith D. Kastella

doi:10.1117/12.217695

1 September 1995 Discrimination gain to optimize detection and classification

Keith D. Kastella

Proceedings Volume 2561, Signal and Data Processing of Small Targets 1995; (1995) https://doi.org/10.1117/12.217695
Event: SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, 1995, San Diego, CA, United States

Abstract

A method for managing agile sensors to optimize detection and classification based on discrimination gain is presented. Expected discrimination gain is used to determine threshold settings and search order for a collection of discrete detection cells. This is applied in a low signal-to-noise environment where target-containing cells must be sampled many times before a target can be detected or classified with high confidence. Bayes rule is used to compute the expected discrimination gain for each sample region using estimated probability that it contains a target. This gain is used to select the optimal cell for the next sample. The effectiveness of this approach was assessed in a simple test case by comparing the result of discrimination optimized search with direct search. For a single 0 dB Gaussian target, the error rate for discrimination optimized search was similar to the direct search result against a 6 dB target.

Citation Download Citation

Keith D. Kastella "Discrimination gain to optimize detection and classification", Proc. SPIE 2561, Signal and Data Processing of Small Targets 1995, (1 September 1995); https://doi.org/10.1117/12.217695

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