Citation: Wang, B.; Wang, S.; Zeng,
D.; Wang, M. Convolutional Neural
Network-Based Radar Antenna
Scanning Period Recognition.
Electronics 2022, 11, 1383. https://
doi.org/10.3390/electronics11091383
Academic Editors: Nunzio Cennamo
and Sung Jin Yoo
Received: 20 March 2022
Accepted: 23 April 2022
Published: 26 April 2022
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Article
Convolutional Neural Network-Based Radar Antenna Scanning
Period Recognition
Bin Wang
1,2
, Shunan Wang
2
, Dan Zeng
1
and Min Wang
1,
*
1
Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai Institute for Advanced
Communication and Data Science, Shanghai University, 99 Shangda Road, Shanghai 200444, China;
wangbin_cetc51@163.com (B.W.); dzeng@shu.edu.cn (D.Z.)
2
51st Institute of China Electronics Technology Group Corporation, 185 Huyi Highway,
Shanghai 200444, China; wang.sn@163.com
* Correspondence: wangmin@mail.shu.edu.cn
Abstract:
The antenna scanning period (ASP) of radar is a crucial parameter in electronic warfare (EW)
which is used in many applications, such as radar work pattern recognition and emitter recognition.
For antennas of radars and EW systems, which perform scanning circularly, the method based on
threshold measurement is invalid. To overcome this shortcoming, this study proposes a method
using the convolutional neural network (CNN) to recognize the ASP of radar under the condition
that antennas of the radar and EW system both scan circularly. A system model is constructed,
and factors affecting the received signal power are analyzed. A CNN model for rapid and accurate
ASP radar classification is developed. A large number of received signal time–power images of
three separate ASPs are used for the training and testing of the developed model under different
experimental conditions. Numerical experiment results and performance comparison demonstrate
high classification accuracy and effectiveness of the proposed method in the condition that antennas
of radar and EW system are circular scan, where the average recognition accuracy for radar ASP is at
least 90% when the signal to-noise ratio (SNR) is not less than 30 dB, which is significantly higher
than the recognition accuracy of NAC and AFT methods based on adaptive threshold detection.
Keywords:
radar antenna scanning period recognition; convolutional neural network; scan circularly;
electronic warfare
1. Introduction
With the development of integrated radio frequency systems, it has been common
for radar and electronic warfare (EW) equipment to share the same antenna [
1
]. To cover
the 360-degree airspace, a radar antenna often scans circularly [
2
]. When the EW system
antenna scans circularly as well as the radar antenna, this is not beneficial to the EW system
to estimate the antenna scanning period (ASP) of a hostile radar. In reality, radars can
have different values of ASP, and different ASP values correspond to different operating
modes [
2
]. Therefore, to achieve precise reconnaissance for specific radar, the EW system
must determine the ASP values in real-time and accurately, which is helpful to deduce
the composition of the enemy defense system and discover monitoring blind spots [
3
,
4
].
Moreover, it is useful to set parameters of a jammer [
3
]. A change in the ASP is also crucial
in determining the threat levels of a radar [
5
]. Thus, it is significant that the EW system
estimates radar ASP when an antenna scans circularly.
Generally, after sorting the intercepted pulse train, the ASP can be estimated according
to the reference times of different received signals. Assume that the reference time of the
ith pulse set is t
i
, and the reference time of the jth pulse set is t
j
, then the ASP of radar is
calculated as
T =
t
j
− t
i
/
(
j − i
)
[
6
,
7
]. There are three methods that use different pulses
of the received radar signal to evaluate the reference time. The first method is to detect
the rising edge of the first pulse in the received radar pulse set as a reference time [
8
,
9
].
Electronics 2022, 11, 1383. https://doi.org/10.3390/electronics11091383 https://www.mdpi.com/journal/electronics
