Stepped frequency radar target recognition using 1D-CNN

I. Jouny

doi:10.1117/12.2618613

31 May 2022 Stepped frequency radar target recognition using 1D-CNN

I. Jouny

Proceedings Volume 12096, Automatic Target Recognition XXXII; 120960C (2022) https://doi.org/10.1117/12.2618613
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Real radar returns from four small scale commercial aircraft models are used to train and test a convolutional neural network target recognition system. Many target recognition systems convert the one dimensional stepped-frequency features into two-dimensional using tools such as spectrograms and scalograms, and thereby utilize a two-dimensional CNN. In this paper, a one-dimensional convolutional neural net is used. The unknown target’s azimuth position may be known completely or within a certain range. The recognition performance is compared with that of an optimal Bayesian classifier assuming complete statistical knowledge. A discussion of the advantages and disadvantages of using 1D-CNN is presented.

Conference Presentation

Citation Download Citation

I. Jouny "Stepped frequency radar target recognition using 1D-CNN", Proc. SPIE 12096, Automatic Target Recognition XXXII, 120960C (31 May 2022); https://doi.org/10.1117/12.2618613

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