Adaptive Kalman Filter Fusion Positioning Based on Wi-Fi and Vision
The fusion of multiple sensor data to improve positioning accuracy and robustness is an important research direction in indoor positioning systems. In this paper, a Wi-Fi- and vision-based Fusion Adaptive Kalman Filter (FAKF) method is proposed for improving the accuracy of indoor positioning. To im...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/3/671 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850068002253307904 |
|---|---|
| author | Shuxin Zhong Li Cheng Haiwen Yuan Xuan Li |
| author_facet | Shuxin Zhong Li Cheng Haiwen Yuan Xuan Li |
| author_sort | Shuxin Zhong |
| collection | DOAJ |
| description | The fusion of multiple sensor data to improve positioning accuracy and robustness is an important research direction in indoor positioning systems. In this paper, a Wi-Fi- and vision-based Fusion Adaptive Kalman Filter (FAKF) method is proposed for improving the accuracy of indoor positioning. To improve the accuracy of Wi-Fi positioning, a random forest algorithm with added region restriction is proposed. For visual positioning, YOLOv7 target detection and Deep SORT target tracking algorithms are combined in order to improve the stability of visual positioning. The fusion positioning method proposed in this study uses Kalman filtering for state estimation and updating by combining measurements from camera and Wi-Fi sensors, and it adaptively adjusts the parameters and weights of the filters by monitoring the residuals of the camera and Wi-Fi measurements in real time in order to optimize the accuracy and stability of the position estimation. In the experimental section, the real trajectory data and the predicted trajectory data generated using different positioning methods are compared. The experimental results show that the fused positioning method can significantly reduce positioning errors and the fused data can more accurately reflect the actual position of a target compared with single-sensor data. |
| format | Article |
| id | doaj-art-1f96da4cbce34dbe8133544ddf81c863 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-1f96da4cbce34dbe8133544ddf81c8632025-08-20T02:48:10ZengMDPI AGSensors1424-82202025-01-0125367110.3390/s25030671Adaptive Kalman Filter Fusion Positioning Based on Wi-Fi and VisionShuxin Zhong0Li Cheng1Haiwen Yuan2Xuan Li3College of Electrical Information, Wuhan Institute of Technology, Wuhan 430205, ChinaCollege of Electrical Information, Wuhan Institute of Technology, Wuhan 430205, ChinaCollege of Electrical Information, Wuhan Institute of Technology, Wuhan 430205, ChinaCollege of Electrical Information, Wuhan Institute of Technology, Wuhan 430205, ChinaThe fusion of multiple sensor data to improve positioning accuracy and robustness is an important research direction in indoor positioning systems. In this paper, a Wi-Fi- and vision-based Fusion Adaptive Kalman Filter (FAKF) method is proposed for improving the accuracy of indoor positioning. To improve the accuracy of Wi-Fi positioning, a random forest algorithm with added region restriction is proposed. For visual positioning, YOLOv7 target detection and Deep SORT target tracking algorithms are combined in order to improve the stability of visual positioning. The fusion positioning method proposed in this study uses Kalman filtering for state estimation and updating by combining measurements from camera and Wi-Fi sensors, and it adaptively adjusts the parameters and weights of the filters by monitoring the residuals of the camera and Wi-Fi measurements in real time in order to optimize the accuracy and stability of the position estimation. In the experimental section, the real trajectory data and the predicted trajectory data generated using different positioning methods are compared. The experimental results show that the fused positioning method can significantly reduce positioning errors and the fused data can more accurately reflect the actual position of a target compared with single-sensor data.https://www.mdpi.com/1424-8220/25/3/671indoor positioningadaptive Kalman filteringWi-Fi positioningvisual positioning |
| spellingShingle | Shuxin Zhong Li Cheng Haiwen Yuan Xuan Li Adaptive Kalman Filter Fusion Positioning Based on Wi-Fi and Vision Sensors indoor positioning adaptive Kalman filtering Wi-Fi positioning visual positioning |
| title | Adaptive Kalman Filter Fusion Positioning Based on Wi-Fi and Vision |
| title_full | Adaptive Kalman Filter Fusion Positioning Based on Wi-Fi and Vision |
| title_fullStr | Adaptive Kalman Filter Fusion Positioning Based on Wi-Fi and Vision |
| title_full_unstemmed | Adaptive Kalman Filter Fusion Positioning Based on Wi-Fi and Vision |
| title_short | Adaptive Kalman Filter Fusion Positioning Based on Wi-Fi and Vision |
| title_sort | adaptive kalman filter fusion positioning based on wi fi and vision |
| topic | indoor positioning adaptive Kalman filtering Wi-Fi positioning visual positioning |
| url | https://www.mdpi.com/1424-8220/25/3/671 |
| work_keys_str_mv | AT shuxinzhong adaptivekalmanfilterfusionpositioningbasedonwifiandvision AT licheng adaptivekalmanfilterfusionpositioningbasedonwifiandvision AT haiwenyuan adaptivekalmanfilterfusionpositioningbasedonwifiandvision AT xuanli adaptivekalmanfilterfusionpositioningbasedonwifiandvision |