ITM Web Conf.
Volume 48, 2022The 4th International Conference on Computing and Wireless Communication Systems (ICCWCS 2022)
|Number of page(s)||8|
|Section||Antennas & Propagation|
|Published online||02 September 2022|
A Two-Stage Support Vector Machine and SqueezeNet System for Range-Angle and Range-Speed Estimation in a Cluttered Environment of Automotive MIMO Radar Systems
1 Hassan I University Settat, Morocco
2 Royal Military College of Canada, Kingston, Ontario, Canada
This paper proposes a two-stage deep-learning approach for frequency modulated continuous waveform multiple‐input multiple‐output (FMCW MIMO) radar embedded in cluttered and jammed environments. The first stage uses the support vector machine (SVM) as a feature extractor that discriminates targets from clutters and jammers. In the second stage, the angle, range, and Doppler estimations of the extracted targets are treated by the SqueezeNet deep convolutional neural network (DCNN) as a multilabel classification problem. The performance of the proposed hybrid SVM-SqueezeNet method is very close to the one achieved by the SqueezeNet only but with the advantage of identifying the type of targets and reducing the training time required by the SqueezeNet.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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