Simultaneous Tracking and Recognizing Drone Targets with Millimeter-Wave Radar and Convolutional Neural Network
In this paper, a framework for simultaneous tracking and recognizing drone targets using a low-cost and small-sized millimeter-wave radar is presented. The radar collects the reflected signals of multiple targets in the field of view, including drone and non-drone targets. The analysis of the receiv...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
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| Series: | Applied System Innovation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-5577/6/4/68 |
| _version_ | 1797585563597930496 |
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| author | Suhare Solaiman Emad Alsuwat Rajwa Alharthi |
| author_facet | Suhare Solaiman Emad Alsuwat Rajwa Alharthi |
| author_sort | Suhare Solaiman |
| collection | DOAJ |
| description | In this paper, a framework for simultaneous tracking and recognizing drone targets using a low-cost and small-sized millimeter-wave radar is presented. The radar collects the reflected signals of multiple targets in the field of view, including drone and non-drone targets. The analysis of the received signals allows multiple targets to be distinguished because of their different reflection patterns. The proposed framework consists of four processes: signal processing, cloud point clustering, target tracking, and target recognition. Signal processing translates the raw collected signals into spare cloud points. These points are merged into several clusters, each representing a single target in three-dimensional space. Target tracking estimates the new location of each detected target. A novel convolutional neural network model was designed to extract and recognize the features of drone and non-drone targets. For the performance evaluation, a dataset collected with an IWR6843ISK mmWave sensor by Texas Instruments was used for training and testing the convolutional neural network. The proposed recognition model achieved accuracies of 98.4% and 98.1% for one and two targets, respectively. |
| first_indexed | 2024-03-11T00:08:55Z |
| format | Article |
| id | doaj.art-d66138e7d3cc4b0d821c3428f38e3804 |
| institution | Directory Open Access Journal |
| issn | 2571-5577 |
| language | English |
| last_indexed | 2024-03-11T00:08:55Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied System Innovation |
| spelling | doaj.art-d66138e7d3cc4b0d821c3428f38e38042023-11-19T00:11:35ZengMDPI AGApplied System Innovation2571-55772023-08-01646810.3390/asi6040068Simultaneous Tracking and Recognizing Drone Targets with Millimeter-Wave Radar and Convolutional Neural NetworkSuhare Solaiman0Emad Alsuwat1Rajwa Alharthi2Department of Computer Sciences, College of Computers and Information Technology, Taif University, Taif 26571, Saudi ArabiaDepartment of Computer Sciences, College of Computers and Information Technology, Taif University, Taif 26571, Saudi ArabiaDepartment of Computer Sciences, College of Computers and Information Technology, Taif University, Taif 26571, Saudi ArabiaIn this paper, a framework for simultaneous tracking and recognizing drone targets using a low-cost and small-sized millimeter-wave radar is presented. The radar collects the reflected signals of multiple targets in the field of view, including drone and non-drone targets. The analysis of the received signals allows multiple targets to be distinguished because of their different reflection patterns. The proposed framework consists of four processes: signal processing, cloud point clustering, target tracking, and target recognition. Signal processing translates the raw collected signals into spare cloud points. These points are merged into several clusters, each representing a single target in three-dimensional space. Target tracking estimates the new location of each detected target. A novel convolutional neural network model was designed to extract and recognize the features of drone and non-drone targets. For the performance evaluation, a dataset collected with an IWR6843ISK mmWave sensor by Texas Instruments was used for training and testing the convolutional neural network. The proposed recognition model achieved accuracies of 98.4% and 98.1% for one and two targets, respectively.https://www.mdpi.com/2571-5577/6/4/68mmWave radarcloud pointstarget trackingtarget recognition |
| spellingShingle | Suhare Solaiman Emad Alsuwat Rajwa Alharthi Simultaneous Tracking and Recognizing Drone Targets with Millimeter-Wave Radar and Convolutional Neural Network Applied System Innovation mmWave radar cloud points target tracking target recognition |
| title | Simultaneous Tracking and Recognizing Drone Targets with Millimeter-Wave Radar and Convolutional Neural Network |
| title_full | Simultaneous Tracking and Recognizing Drone Targets with Millimeter-Wave Radar and Convolutional Neural Network |
| title_fullStr | Simultaneous Tracking and Recognizing Drone Targets with Millimeter-Wave Radar and Convolutional Neural Network |
| title_full_unstemmed | Simultaneous Tracking and Recognizing Drone Targets with Millimeter-Wave Radar and Convolutional Neural Network |
| title_short | Simultaneous Tracking and Recognizing Drone Targets with Millimeter-Wave Radar and Convolutional Neural Network |
| title_sort | simultaneous tracking and recognizing drone targets with millimeter wave radar and convolutional neural network |
| topic | mmWave radar cloud points target tracking target recognition |
| url | https://www.mdpi.com/2571-5577/6/4/68 |
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