Indoor personnel detection and tracking of millimeter-wave radar based on improved DBSCAN algorithm
With the progress of technology and the enhancement of social demand for privacy protection, optical monitoring equipment has gradually caused public concern. In contrast, millimeter-wave(mmWave) radar monitoring has been rapidly developed because of its superiority in privacy. However, the indoor e...
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| Vydáno v: | Engineering Research Express Ročník 7; číslo 2; s. 25220 - 25235 |
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30.06.2025
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| ISSN: | 2631-8695, 2631-8695 |
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| Abstract | With the progress of technology and the enhancement of social demand for privacy protection, optical monitoring equipment has gradually caused public concern. In contrast, millimeter-wave(mmWave) radar monitoring has been rapidly developed because of its superiority in privacy. However, the indoor environment is relatively complex, and traditional density-based clustering algorithms perform poorly in accurate tracking. The point cloud data generated from indoor scenario echoes collected by mmWave radar is relatively sparse and accompanied by noise points, which significantly affects tracking performance. In this paper, we propose an improved DBSCAN clustering algorithm that uses a multi-frame aggregation method to suppress multipath effects and eliminate ‘false targets’. It is combined with the extended Kalman filter(EKF) algorithm to form a complete system. In our system, the raw data collected by mmWave radar is processed by fast fourier transform(FFT), static clutter removal and constant false alarm rate(CFAR) to obtain point cloud data. Since the density of point cloud data greatly affects the performance of clustering algorithms, we use multi-frame aggregation method to process the point cloud data to increase its density. Accurate indoor personnel tracking is then achieved through clustering and extended Kalman filtering, and the tracking error is within 0.1 m. |
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| AbstractList | With the progress of technology and the enhancement of social demand for privacy protection, optical monitoring equipment has gradually caused public concern. In contrast, millimeter-wave(mmWave) radar monitoring has been rapidly developed because of its superiority in privacy. However, the indoor environment is relatively complex, and traditional density-based clustering algorithms perform poorly in accurate tracking. The point cloud data generated from indoor scenario echoes collected by mmWave radar is relatively sparse and accompanied by noise points, which significantly affects tracking performance. In this paper, we propose an improved DBSCAN clustering algorithm that uses a multi-frame aggregation method to suppress multipath effects and eliminate ‘false targets’. It is combined with the extended Kalman filter(EKF) algorithm to form a complete system. In our system, the raw data collected by mmWave radar is processed by fast fourier transform(FFT), static clutter removal and constant false alarm rate(CFAR) to obtain point cloud data. Since the density of point cloud data greatly affects the performance of clustering algorithms, we use multi-frame aggregation method to process the point cloud data to increase its density. Accurate indoor personnel tracking is then achieved through clustering and extended Kalman filtering, and the tracking error is within 0.1 m. |
| Author | Gao, Yuan Li, Andong Xing, Mengdao Zhou, Fang |
| Author_xml | – sequence: 1 givenname: Fang orcidid: 0000-0003-1030-510X surname: Zhou fullname: Zhou, Fang organization: University of Birmingham School of Engineering, Birmingham B15 2TT, United Kingdom – sequence: 2 givenname: Yuan orcidid: 0009-0008-9191-1008 surname: Gao fullname: Gao, Yuan organization: School of Computer Science and Information Engineering , Hefei University of Technology, Hefei 230009, People’s Republic of China – sequence: 3 givenname: Andong surname: Li fullname: Li, Andong organization: School of Computer Science and Information Engineering , Hefei University of Technology, Hefei 230009, People’s Republic of China – sequence: 4 givenname: Mengdao surname: Xing fullname: Xing, Mengdao organization: National Key Laboratory of Radar Signal Processing , Xidian University, Xi’an 710071, People’s Republic of China |
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| Title | Indoor personnel detection and tracking of millimeter-wave radar based on improved DBSCAN algorithm |
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