The Presentation of an Algorithm for Interference Detection in the Synthetic Aperture Radar

Document Type : Original Article

Authors

1 Assistant Professor, Faculty of Electrical Engineering, Shahid Sattari University of Aeronautical Sciences and Technology, Tehran, Iran

2 Master student, Faculty of Electrical Engineering, Shahid Sattari University of Aeronautical Sciences and Technology, Tehran, Iran

3 Associate Professor, Faculty of Electrical Engineering, Shahid Sattari University of Aeronautical Sciences and Technology, Tehran, Iran

Abstract

The synthetic aperture radar is an imaging radar that has a high resolution. The synthetic
aperture radar image may be degraded by the interference of radio frequencies and an
incomprehensible image may be created. Interferences in the synthetic aperture radars are
divided into the three categories of , , and , which represent radio frequency noise interference,
narrow band interference and wideband interference, respectively. To effectively reduce the
interference in synthetic aperture radar images, first the presence of interference and its type
should be asserted and then the interference reduction algorithms should be calculated according
to interference type. In this paper an algorithm for the detection of interference and its type in
the synthetic aperture radar images is presented. Whilst in the previous articles the SSD method
is used for interference detection, in this paper we have used the Faster RCNN method based on
neural network convolutional which has a higher speed and accuracy than the SSD method. In
this method, first a neural network is trained with the ability of multiple classification. Then the
Faster RCNN is constructed with the neural network and and is trained by 25 time - frequency
images from the artificial aperture radar signal. The trained network is able to detect any
interference in the radar signal of a synthetic window with 99% accuracy. After detecting the
interference by the proposed algorithm, the normalized least mean square filter is able to reduce
the interference and improve the radar image. This filter operates similarly in decreasing all
three types of interference.

Keywords

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References

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Radar
Volume 9, Issue 1 - Serial Number 25
September 2021
Pages 107-117

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History

  • Receive Date: 18 June 2021
  • Revise Date: 10 October 2021
  • Accept Date: 04 December 2021
  • Publish Date: 23 August 2021

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