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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Elektronika dan Telekomunikasi
Wael, Chaeriah Bin Ali
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Computer Science & IT Electrical & Electronics Engineering Engineering

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APPENDIX VOL. 19 NO. 2 Wael, Chaeriah Bin Ali
Jurnal Elektronika dan Telekomunikasi Vol 19, No 2 (2019)
Publisher : Indonesian Institute of Sciences

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BACK COVER VOL. 19 NO. 2 Wael, Chaeriah Bin Ali
Jurnal Elektronika dan Telekomunikasi Vol 19, No 2 (2019)
Publisher : Indonesian Institute of Sciences

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FRONT COVER VOL. 19 NO. 2 Wael, Chaeriah Bin Ali
Jurnal Elektronika dan Telekomunikasi Vol 19, No 2 (2019)
Publisher : Indonesian Institute of Sciences

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PREFACE VOL. 19 NO. 2 Wael, Chaeriah Bin Ali
Jurnal Elektronika dan Telekomunikasi Vol 19, No 2 (2019)
Publisher : Indonesian Institute of Sciences

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Autonomous radar interference detection and mitigation using neural network and signal decomposition Kurniawan, Dayat; Rohman, Budiman Putra Asmaur; Indrawijaya, Ratna; Wael, Chaeriah Bin Ali; Suyoto, Suyoto; Adhi, Purwoko; Firmansyah, Iman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2854-2861

Abstract

Autonomous radar interference is a challenging problem in autonomous vehicle systems. Interference signals can decrease the signal-to-interference-noise ratio (SINR), and this condition decreases the performance detection of autonomous radar. This paper exploits a neural network and signal decomposition to detect and mitigate radar interference in autonomous vehicle applications. A neural network (NN) with four inputs, one hidden layer, and one output is trained with various signal-to-noise (SNR), interference radar bandwidth, and sweep time of autonomous radar. Four inputs of NN represent SNR, mean, total harmonic distortion (THD), and root means square (RMS) of the received radar signal. Variational mode decomposition (VMD) and zeroing based on a constant false alarm rate (CFAR-Z) are used to mitigate radar interference. VMD algorithm is applied to decompose interference signals into multi-frequency sub-band. As a result, the proposed neural network can detect radar interference, and NN-VMD-CFAR-Z can increase SINR up to 2dB higher than the NN-CFAR-Z algorithm.
Co-Authors Budiman Putra Asmaur Rohman Dayat Kurniawan Iman Firmansyah Indrawijaya, Ratna Purwoko Adhi Siti Helmyati