A Machine Learning Based Vehicle Classification in Forward Scattering Radar
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 compariso...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9795288/ |
| _version_ | 1811332568382963712 |
|---|---|
| author | Mohammed E. A. Kanona Mohamad Y. Alias Mohamed Khalafalla Hassan Khalid S. Mohamed Mutaz H. H. Khairi Mosab Hamdan Yassin A. Hamdalla Omnia M. Osman Ahmed M. O. Ahmed |
| author_facet | Mohammed E. A. Kanona Mohamad Y. Alias Mohamed Khalafalla Hassan Khalid S. Mohamed Mutaz H. H. Khairi Mosab Hamdan Yassin A. Hamdalla Omnia M. Osman Ahmed M. O. Ahmed |
| author_sort | Mohammed E. A. Kanona |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-04-13T16:38:52Z |
| format | Article |
| id | doaj.art-4e5b500e3d8941f8896c17467e44b244 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T16:38:52Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4e5b500e3d8941f8896c17467e44b2442022-12-22T02:39:20ZengIEEEIEEE Access2169-35362022-01-0110646886470010.1109/ACCESS.2022.31831279795288A Machine Learning Based Vehicle Classification in Forward Scattering RadarMohammed E. A. Kanona0https://orcid.org/0000-0002-6316-7663Mohamad Y. Alias1https://orcid.org/0000-0002-0766-7911Mohamed Khalafalla Hassan2https://orcid.org/0000-0002-6238-8915Khalid S. Mohamed3https://orcid.org/0000-0001-5835-4299Mutaz H. H. Khairi4https://orcid.org/0000-0002-5904-7642Mosab Hamdan5Yassin A. Hamdalla6Omnia M. Osman7Ahmed M. O. Ahmed8IoT Research and Development Center, Future University, Khartoum, SudanFaculty of Engineering, Multimedia University, Cyberjaya, MalaysiaFaculty of Telecommunication and Space Technology, Future University, Khartoum, SudanInnovation, Research and Development Center, Future University, Khartoum, SudanFaculty of Engineering, Future University, Khartoum, SudanFaculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, MalaysiaFaculty of Telecommunication and Space Technology, Future University, Khartoum, SudanFaculty of Telecommunication and Space Technology, Future University, Khartoum, SudanFaculty of Telecommunication and Space Technology, Future University, Khartoum, SudanThe 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%.https://ieeexplore.ieee.org/document/9795288/Forward scatteringpre-processingtarget classificationmachine learningKNN |
| spellingShingle | Mohammed E. A. Kanona Mohamad Y. Alias Mohamed Khalafalla Hassan Khalid S. Mohamed Mutaz H. H. Khairi Mosab Hamdan Yassin A. Hamdalla Omnia M. Osman Ahmed M. O. Ahmed A Machine Learning Based Vehicle Classification in Forward Scattering Radar IEEE Access Forward scattering pre-processing target classification machine learning KNN |
| title | A Machine Learning Based Vehicle Classification in Forward Scattering Radar |
| title_full | A Machine Learning Based Vehicle Classification in Forward Scattering Radar |
| title_fullStr | A Machine Learning Based Vehicle Classification in Forward Scattering Radar |
| title_full_unstemmed | A Machine Learning Based Vehicle Classification in Forward Scattering Radar |
| title_short | A Machine Learning Based Vehicle Classification in Forward Scattering Radar |
| title_sort | machine learning based vehicle classification in forward scattering radar |
| topic | Forward scattering pre-processing target classification machine learning KNN |
| url | https://ieeexplore.ieee.org/document/9795288/ |
| work_keys_str_mv | AT mohammedeakanona amachinelearningbasedvehicleclassificationinforwardscatteringradar AT mohamadyalias amachinelearningbasedvehicleclassificationinforwardscatteringradar AT mohamedkhalafallahassan amachinelearningbasedvehicleclassificationinforwardscatteringradar AT khalidsmohamed amachinelearningbasedvehicleclassificationinforwardscatteringradar AT mutazhhkhairi amachinelearningbasedvehicleclassificationinforwardscatteringradar AT mosabhamdan amachinelearningbasedvehicleclassificationinforwardscatteringradar AT yassinahamdalla amachinelearningbasedvehicleclassificationinforwardscatteringradar AT omniamosman amachinelearningbasedvehicleclassificationinforwardscatteringradar AT ahmedmoahmed amachinelearningbasedvehicleclassificationinforwardscatteringradar AT mohammedeakanona machinelearningbasedvehicleclassificationinforwardscatteringradar AT mohamadyalias machinelearningbasedvehicleclassificationinforwardscatteringradar AT mohamedkhalafallahassan machinelearningbasedvehicleclassificationinforwardscatteringradar AT khalidsmohamed machinelearningbasedvehicleclassificationinforwardscatteringradar AT mutazhhkhairi machinelearningbasedvehicleclassificationinforwardscatteringradar AT mosabhamdan machinelearningbasedvehicleclassificationinforwardscatteringradar AT yassinahamdalla machinelearningbasedvehicleclassificationinforwardscatteringradar AT omniamosman machinelearningbasedvehicleclassificationinforwardscatteringradar AT ahmedmoahmed machinelearningbasedvehicleclassificationinforwardscatteringradar |