Title :

Autonomous radar interference detection and mitigation using neural network and signal decomposition

Author(s) :

Kurniawan, Dayat [Auteur]
National Research and Innovation Agency [BRIN]
Asmaur Rohman, Budiman Putra [Auteur]
National Research and Innovation Agency [BRIN]
Indrawijaya, Ratna [Auteur]
National Research and Innovation Agency [BRIN]
Wael, Chaeriah Bin Ali [Auteur]
Université Polytechnique Hauts-de-France [UPHF]
National Research and Innovation Agency [BRIN]
Matériaux et Acoustiques pour MIcro et NAno systèmes intégrés - IEMN [MAMINA - IEMN]
Suyoto, Suyoto [Auteur]
National Research and Innovation Agency [BRIN]
Adhi, Purwoko [Auteur]
National Research and Innovation Agency [BRIN]
Firmansyah, Iman [Auteur]
National Research and Innovation Agency [BRIN]

Journal title :

IAES International Journal of Artificial Intelligence (IJ-AI)

Pages :

2854-2861

Publisher :

University of Leicester, United Kingdom

Publication date :

2024

ISSN :

2089-4872

English keyword(s) :

Autonomous radar
Detection Interference
Neural network
Signal decomposition

HAL domain(s) :

Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Réseau de neurones [cs.NE]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Popular science :

Non

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL

Submission date :

2024-08-20T02:35:11Z