mmWave Radar Sensors Fusion for Indoor Object Detection and Tracking
Indoor object detection and tracking using millimeter-wave (mmWave) radar sensors have received much attention recently due to the emergence of applications of energy assignment, privacy, health, and safety. Increasing the valid field of view of the system and accuracy through multi-sensors is criti...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/14/2209 |
| _version_ | 1797433708200853504 |
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| author | Xu Huang Joseph K. P. Tsoi Nitish Patel |
| author_facet | Xu Huang Joseph K. P. Tsoi Nitish Patel |
| author_sort | Xu Huang |
| collection | DOAJ |
| description | Indoor object detection and tracking using millimeter-wave (mmWave) radar sensors have received much attention recently due to the emergence of applications of energy assignment, privacy, health, and safety. Increasing the valid field of view of the system and accuracy through multi-sensors is critical to achieving an efficient tracking system. This paper uses two mmWave radar sensors for accurate object detection and tracking: two noise reduction stages to reduce noise and distinguish cluster groups. The presented data fusion method effectively estimates the transformation of the data alignment and synchronizes the result that can allow us to visualize the objects’ information acquired by one radar on another one. An efficient density-based clustering algorithm to provide high clustering accuracy is presented. The Unscented Kalman Filter tracking algorithm with data association tracks multiple objects simultaneously in terms of accuracy and timing. Furthermore, an indoor object tracking system is developed based on our proposed method. Finally, the proposed method is validated by comparing it with our previous system and a commercial system. The experimental results demonstrate that the proposed method’s advantage is of positive significance for handling the effect of occlusions at higher numbers of weak data and for detecting and tracking each object more accurately. |
| first_indexed | 2024-03-09T10:20:51Z |
| format | Article |
| id | doaj.art-fbba0706b99d479992bf0f38cd0b29ff |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T10:20:51Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-fbba0706b99d479992bf0f38cd0b29ff2023-12-01T22:05:26ZengMDPI AGElectronics2079-92922022-07-011114220910.3390/electronics11142209mmWave Radar Sensors Fusion for Indoor Object Detection and TrackingXu Huang0Joseph K. P. Tsoi1Nitish Patel2Department of Electrical and Computer Engineering, The University of Auckland, Auckland 1010, New ZealandDepartment of Electrical and Computer Engineering, The University of Auckland, Auckland 1010, New ZealandDepartment of Electrical and Computer Engineering, The University of Auckland, Auckland 1010, New ZealandIndoor object detection and tracking using millimeter-wave (mmWave) radar sensors have received much attention recently due to the emergence of applications of energy assignment, privacy, health, and safety. Increasing the valid field of view of the system and accuracy through multi-sensors is critical to achieving an efficient tracking system. This paper uses two mmWave radar sensors for accurate object detection and tracking: two noise reduction stages to reduce noise and distinguish cluster groups. The presented data fusion method effectively estimates the transformation of the data alignment and synchronizes the result that can allow us to visualize the objects’ information acquired by one radar on another one. An efficient density-based clustering algorithm to provide high clustering accuracy is presented. The Unscented Kalman Filter tracking algorithm with data association tracks multiple objects simultaneously in terms of accuracy and timing. Furthermore, an indoor object tracking system is developed based on our proposed method. Finally, the proposed method is validated by comparing it with our previous system and a commercial system. The experimental results demonstrate that the proposed method’s advantage is of positive significance for handling the effect of occlusions at higher numbers of weak data and for detecting and tracking each object more accurately.https://www.mdpi.com/2079-9292/11/14/2209millimeterwaveradardetectingobject clusteringsensor fusiontracking |
| spellingShingle | Xu Huang Joseph K. P. Tsoi Nitish Patel mmWave Radar Sensors Fusion for Indoor Object Detection and Tracking Electronics millimeterwave radar detecting object clustering sensor fusion tracking |
| title | mmWave Radar Sensors Fusion for Indoor Object Detection and Tracking |
| title_full | mmWave Radar Sensors Fusion for Indoor Object Detection and Tracking |
| title_fullStr | mmWave Radar Sensors Fusion for Indoor Object Detection and Tracking |
| title_full_unstemmed | mmWave Radar Sensors Fusion for Indoor Object Detection and Tracking |
| title_short | mmWave Radar Sensors Fusion for Indoor Object Detection and Tracking |
| title_sort | mmwave radar sensors fusion for indoor object detection and tracking |
| topic | millimeterwave radar detecting object clustering sensor fusion tracking |
| url | https://www.mdpi.com/2079-9292/11/14/2209 |
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