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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Indrawijaya, Ratna
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Robust automotive radar interference mitigation using multiplicative-adaptive filtering and Hilbert transform Asmaur Rohman, Budiman Putra; Suryadi Satyawan, Arief; Kurniawan, Dayat; Indrawijaya, Ratna; Bin Ali Wael, Chaeriah; Armi, Nasrullah
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp326-336

Abstract

Radar is one of the sensors that have significant attention to be implemented in an autonomous vehicle since its robustness under many possible environmental conditions such as fog, rain, and poor light. However, the implementation risks interference because of transmitting and/or receiving radar signals from/to other vehicles. This interference will increase the floor noise that can mask the target signal. This paper proposes multiplicative-adaptive filtering and Hilbert transform to mitigate the interference effect and maintain the target signal detectability. The method exploited the trade-off between the step-size and sidelobe effect on the least mean square-based adaptive filtering to improve the target detection accuracy, especially in the long-range case. The numerical analysis on the millimeter-wave frequency modulated continuous wave radar with multiple interferers concluded that the proposed method could maintain and enhance the target signal even if the target range is relatively far from the victim radar.
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 Bin Ali Wael, Chaeriah Budiman Putra Asmaur Rohman Dayat Kurniawan Iman Firmansyah Nasrullah Armi Nishimoto, Masahiko Purwoko Adhi Siti Helmyati Sukoco, Bagus Edy Suryadi Satyawan, Arief Wael, Chaeriah Bin Ali