Article
Specific Radar Recognition Based on Characteristics of Emitted
Radio Waveforms Using Convolutional Neural Networks
Jan Matuszewski * and Dymitr Pietrow
Citation: Matuszewski, J.; Pietrow, D.
Specific Radar Recognition Based on
Characteristics of Emitted Radio
Waveforms Using Convolutional
Neural Networks. Sensors 2021, 21,
8237. https://doi.org/10.3390/
s21248237
Academic Editors: Nunzio Cennamo,
Yangquan Chen, Subhas
Mukhopadhyay, Mohamed
Jamal Deen, Junseop Lee and
Simone Morais
Received: 22 October 2021
Accepted: 3 December 2021
Published: 9 December 2021
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Institute of Radioelectronics, Faculty of Electronics, Military University of Technology, 00-908 Warsaw, Poland;
dymitr.pietrow@wat.edu.pl
* Correspondence: jan.matuszewski@wat.edu.pl; Tel.: +48-261-837-571
Abstract:
With the increasing complexity of the electromagnetic environment and continuous devel-
opment of radar technology we can expect a large number of modern radars using agile waveforms
to appear on the battlefield in the near future. Effectively identifying these radar signals in electronic
warfare systems only by relying on traditional recognition models poses a serious challenge. In
response to the above problem, this paper proposes a recognition method of emitted radar signals
with agile waveforms based on the convolutional neural network (CNN). These signals are measured
in the electronic recognition receivers and processed into digital data, after which they undergo
recognition. The implementation of this system is presented in a simulation environment with the
help of a signal generator that has the ability to make changes in signal signatures earlier recognized
and written in the emitter database. This article contains a description of the software’s components,
learning subsystem and signal generator. The problem of teaching neural networks with the use
of the graphics processing units and the way of choosing the learning coefficients are also outlined.
The correctness of the CNN operation was tested using a simulation environment that verified the
operation’s effectiveness in a noisy environment and in conditions where many radar signals that
interfere with each other are present. The effectiveness results of the applied solutions and the
possibilities of developing the method of learning and processing algorithms are presented by means
of tables and appropriate figures. The experimental results demonstrate that the proposed method
can effectively solve the problem of recognizing raw radar signals with agile time waveforms, and
achieve correct probability of recognition at the level of 92–99%.
Keywords:
convolutional neural networks; radar recognition; deep learning; signal simulation;
electronic warfare
1. Introduction
In radio-electronic reconnaissance systems we receive and then measure the basic
time, frequency and spatial parameters (related to the scanning of the antenna) in order
to recognize their emission sources (in our case, radars), and we do not visualize the
spatial situation using signals. The radio-electronic reconnaissance systems extract the
basic characteristic parameters from measured radar signals. Based on these parameters,
we can obtain information such as the system, application, type and platform of the radar,
and further deduce the battlefield situation, threat level, activity rule, tactical
intention, etc.
,
and provide important intelligence support for our own decision-making system. The
modern electromagnetic environment is considered complex due to a multitude of signals
originating from a number of different radars (emitters), and in the case of signals coming
from the same radar their parameters (features) are measured with low accuracy. In many
cases the radar may change one or several signal parameters in order to perform its task
more efficiently [
1
,
2
]. Since each radar has limited parameter ranges (e.g., transmits within
a limited frequency band) and often identifiable characteristics, it is assumed that radar
signals with similar characteristics originate from the same device [3,4].
Sensors 2021, 21, 8237. https://doi.org/10.3390/s21248237 https://www.mdpi.com/journal/sensors
