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All Journal International Journal of Electrical and Computer Engineering Jurnal Elektronika dan Telekomunikasi
Ratna Indrawijaya
National Research and Innovation Agency
Computer Science & IT Electrical & Electronics Engineering

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Ground Penetrating Radar Data Inversion Using Dual-Input Convolutional Autoencoder for Ferroconcrete Inspection Budiman Putra Asmaur Rohman; Masahiko Nishimoto; Ratna Indrawijaya; Dayat Kurniawan; Iman Firmansyah; Bagus Edy Sukoco
Jurnal Elektronika dan Telekomunikasi Vol. 24 No. 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.642

Abstract

Ground penetrating radar (GPR) is a non-destructive tool for exploring an object buried underground. Currently, GPR is also considered for reinforced concrete inspection. However, the image produced by GPR can not be easily interpreted. Besides, the large observation of building concrete inspection also motivates the researchers to fastening and easing radar image interpretation. Thus,  this research proposes a new method to translate GPR scattering data image to its internal structure visualization. The proposed employs a convolutional autoencoder model using amplitude and phase radar data as input of the algorithm. As evaluation, in this stage, we perform numerical analysis by using finite-difference time-domain-based synthetic data that considers three cases: concrete with rebar, concrete with crack, and concrete with rebar and crack. All of those cases are simulated with randomized dimensions and positions that is possible in the real applications. Compared with the baseline method, our method shows superiority, especially in the semantic segmentation perspective. The parameter size of the proposed model is also much smaller, around one-third of the previous method. Therefore, the method is feasible enough to be implemented in real applications addressing an automatic internal structure reinforced concrete visulaization
Robust automotive radar interference mitigation using multiplicative-adaptive filtering and Hilbert transform Budiman Putra Asmaur Rohman; Arief Suryadi Satyawan; Dayat Kurniawan; Ratna Indrawijaya; Chaeriah Bin Ali Wael; Nasrullah Armi
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.
Co-Authors Arief Suryadi Satyawan Bagus Edy Sukoco Budiman Putra Asmaur Rohman Chaeriah Bin Ali Wael Dayat Kurniawan Iman Firmansyah Masahiko Nishimoto Nasrullah Armi