Neural Net Classifier For Millimeter Wave Radar

Joe R. Brown; Sue Archer; Mark R. Bower

doi:10.1117/12.962373

6 December 1989 Neural Net Classifier For Millimeter Wave Radar

Joe R. Brown, Sue Archer, Mark R. Bower

Proceedings Volume 1154, Real-Time Signal Processing XII; (1989) https://doi.org/10.1117/12.962373
Event: 33rd Annual Technical Symposium, 1989, San Diego, United States

Abstract

This paper describes the development of a neural net classifier for use in an automatic target recognition (ATR) system using millimeter wave (MMW) radar data. Two distinctive neural net classifiers were developed using mapping models (back-propagation and counterpropagation) and compared to a quadratic (Bayesian-like) classifier. A statistical feature set and a radar data set was used for both training and testing all three classifier systems. This statistical feature set is often used to test IMATRs prior to using actual data. Results are presented and indicate that the backpropagation net performed at near 100 percent accuracy for the statistical feature set and slightly outperformed the counterpropagation model in this application. Both networks hold promising results using real radar data.

Citation Download Citation

Joe R. Brown, Sue Archer, and Mark R. Bower "Neural Net Classifier For Millimeter Wave Radar", Proc. SPIE 1154, Real-Time Signal Processing XII, (6 December 1989); https://doi.org/10.1117/12.962373

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