Open AccessImplementation of feature extraction and deep learning-based ensemble classifier for interference mitigation in radar signals
Author(s) -
N. Indira,
M. Venkateswara Rao
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v24.i2.pp1195-1201
Subject(s) - interference (communication) , radar , computer science , classifier (uml) , artificial intelligence , feature extraction , noise (video) , pattern recognition (psychology) , engineering , telecommunications , channel (broadcasting) , image (mathematics)
In automotive vehicles, radar is the one of the component for autonomous driving, used for target detection and long-range sensing. Whereas interference exists in signals, noise increases and it effects severely while detecting target objects. For these reasons, various interference mitigation techniques are implemented in this paper. By using these mitigation techniques interference and noise are reduced and original signals are reconstructed. In this paper, we proposed a method to mitigate interference in signal using deep learning. The proposed method provides the best and accurate performance in relate to the various interference conditions and gives better accuracy compared with other existing methods.