Deep learning for radar signal detection in electronic warfare systems

Date
2020
Editor(s)
Advisor
Supervisor
Co-Advisor
Co-Supervisor
Instructor
Source Title
IEEE National Radar Conference
Print ISSN
Electronic ISSN
Publisher
IEEE
Volume
Issue
Pages
Language
English
Journal Title
Journal ISSN
Volume Title
Series
Abstract

Detection of radar signals is the initial step for passive systems. Since these systems do not have prior information about received signal, application of matched filter and general likelihood ratio tests are infeasible. In this paper, we propose a new method for detecting received pulses automatically with no restriction of having intentional modulation or pulse on pulse situation. Our method utilizes a cognitive detector incorporating bidirectional long-short term memory based deep denoising autoencoders. Moreover, a novel loss function for detection is developed. Performance of the proposed method is compared to two well known detectors, namely: energy detector and time-frequency domain detector. Qualitative experiments show that the proposed method is able to detect presence of a signal with low probability of false alarm and it outperforms the other methods in all signal-to-noise ratio cases.

Course
Other identifiers
Book Title
Citation
Published Version (Please cite this version)