Citation

Kanona, Mohammed E. A. and Alias, Mohamad Yusoff and Hassan, Mohamed Khalafalla and Mohamed, Khalid S. and Khairi, Mutaz H. H. and Hamdan, Mosab and Hamdalla, Yassin A. and Osman, Omnia M. and Ahmed, Ahmed M. O. (2022) A Machine Learning Based Vehicle Classification in Forward Scattering Radar. IEEE Access, 10. pp. 64688-64700. ISSN 2169-3536

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Abstract

The Forward scattering radars (FSRs) are special types of Bistatic radars in which detected targets should exist in the narrow baseline to obtain their tracking at an angle of 180 degree. This gives the radar several features such as target classification which makes FSR more privileged in comparison to traditional radar systems. Existing research works concerning the ground target detection and classification have utilized neural network for the identification processes and compared it to other statistical models in terms of signal complexity. However, these works considered limited number of scenarios and thereby, the results are insufficient to create an automatic classification system. This study investigates and analyses the classification of ground targets in FSR using Machine-learning (ML) techniques, and proposes a hybrid model for ground target classification. The analysis in this paper represent a foundation for a potential use of pre-processing and signal processing techniques, statistical analysis, and ML in radar applications. The obtained results show that the k-nearest neighbor classifier (KNN) achieves the best performance in all examined scenarios. Additionally, combining multiple pre-processing techniques enhances the accuracy of classification by approximately 30.2% and increases the overall accuracy to more than 99%.

Item Type:	Article
Uncontrolled Keywords:	Radar, Scattering, Radar detection, Spaceborne radar, Neural networks
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	29 Jul 2022 01:27
Last Modified:	29 Jul 2022 01:27
URII:	http://shdl.mmu.edu.my/id/eprint/10241

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