A Machine Learning Based Vehicle Classification in Forward Scattering Radar

Author:

Kanona Mohammed E. A.1ORCID,Alias Mohamad Y.2ORCID,Hassan Mohamed Khalafalla3ORCID,Mohamed Khalid S.4ORCID,Khairi Mutaz H. H.5ORCID,Hamdan Mosab6,Hamdalla Yassin A.3,Osman Omnia M.3,Ahmed Ahmed M. O.3

Affiliation:

1. IoT Research and Development Center, Future University, Khartoum, Sudan

2. Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia

3. Faculty of Telecommunication and Space Technology, Future University, Khartoum, Sudan

4. Innovation, Research and Development Center, Future University, Khartoum, Sudan

5. Faculty of Engineering, Future University, Khartoum, Sudan

6. Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia

Funder

Telekom Research & Development Sdn. Bhd. (TM R&D) Project under TM R&D Research Grant, in 2021

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Reference36 articles.

1. Rcs classification on ground moving target using LTE passive bistatic radar;aziz;Journal of Science Research and Development,2016

2. Analysis of different z-score data to the neural network for automatic FSR vehicle classification

3. Ground vehicles classification using multi perspective features in FSR micro-sensor network;abdullah;J Telecommun Electron Comput Eng,2017

4. Wavelet and neural network based ground target classification in forward scattering radar;ahmed,2018

5. Detection of ground target in forward scattering radar using Hilbert transform and wavelet technique;alla;Int Rev Electr Eng -I,2009

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