Three-Dimensional Device-Free Localization for Vehicle
Abstract
:1. Introduction
2. Related Works
- We propose a screening method for blocked communication links based on the Gaussian kernel distance to differentiate whether a link is obscured or not by the vehicle in a cluttered environment.
- We propose the use of information entropy (IE) to evaluate the contribution of the obscured links; according to the contribution to the vehicle localization, different weights are assigned to each obscured link.
- We propose a combination weight allocation method based on the link weight and voxel spatial covariance to each voxel. Relying on the combination weight of each voxel, we generate a heat map of the monitoring area and estimate the location and the 3D information of the vehicle.
- We evaluate the proposed techniques with extensive experiments and analysis in a real testbed.
3. The Theory of 3D-DFL for Roadside Vehicle
3.1. The Overview of DFL for a Vehicle Based on WSN
3.2. The Voxels Partition and Its 2D Plane Projection
3.3. The Procedure of This Work
- We count the real-time RSS (RT-RSS) values of communication links in a period of time and generate the online-phase RSS histograms as .
- We calculate the kernel distance between the online-phase and offline-phase histograms of each communication link and compare the kernel distances with a threshold ; then, obtain the state (obscured or not obscured) of every communication link, expressed as a matrix S.
- We utilize the information entropy principle to calculate the communication link weight matrix , which is adopted to distinguish the contribution of the different obscured communication links to the 3D-DFL system.
- We divide the monitored area into 3D voxels and project them on the X-O-Y plane and the X-O-Z plane, expressed as and .
- We obtain the voxel weight matrix by multiplying the voxels’ space covariance of the X-O-Y plane, the matrix , the matrix and the voxel matrix in the same way, we can obtain the voxel weight matrix according to the product of the voxels’ space covariance in the X-O-Y plane, the matrix , the matrix and the voxel matrix .
- We estimate the length and the width of the vehicle by taking an appropriate threshold for the heat map contours of the weight matrix ; in the same way, we can obtain the height of the vehicle from the weight matrix .
4. The Selection Method for the Obscured Link
4.1. RSS Characteristics Based on a Statistics Histogram
4.2. The Selecting Method Based on Kernel Distance
5. The Location and Three-Dimensional Estimation of the Vehicle
5.1. The Weight Allocation Method Based on the Information Entropy Principle for the Obscured Links
5.2. The Space Covariance of Voxels
5.3. The Location and the 3D Information of the Vehicle
5.3.1. The Voxel Weight Based on the Link Weight and the Spatial Covariance
5.3.2. The Location and the 3D Information Estimation Based on Contour Threshold
6. Experiments
6.1. Experimental Setup
6.2. Results and Discussion
6.3. The Influence of Each Parameter to the Error of the Prediction Results
6.3.1. Effect of Gaussian Kernel Width Parameter on Link Screening Results
6.3.2. The Influence of and to the Weight Allocation Method Based on the IE Method
6.4. The Influence of the Sensor Network Topology on the Localization Accuracy
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
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Parameter | Value | Description |
---|---|---|
30 | Gaussian kernel width | |
1 | Customized parameter in the IE method | |
0.24 | Customized parameter in the IE method | |
1 | Variance of each voxel in the covariance model | |
0.9 | Space constant of the covariance model |
ID | Real Coordinates of the Vehicle (m) | Predicted Coordinates of the Vehicle (m) | RMSE (m) |
---|---|---|---|
1 | (0, 2) | (1.80, 2.27) | 1.820 |
2 | (4, 2) | (3.94, 2.24) | 0.247 |
3 | (8, 2) | (7.99, 2.25) | 0.250 |
4 | (12, 2) | (11.89, 2.23) | 0.255 |
5 | (16, 2) | (14.57, 2.33) | 1.468 |
Average | – | – | 0.808 |
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Wang, M.; Yang, J.; Huang, B.; Yang, Y.; Xu, Y. Three-Dimensional Device-Free Localization for Vehicle. Sensors 2020, 20, 3775. https://doi.org/10.3390/s20133775
Wang M, Yang J, Huang B, Yang Y, Xu Y. Three-Dimensional Device-Free Localization for Vehicle. Sensors. 2020; 20(13):3775. https://doi.org/10.3390/s20133775
Chicago/Turabian StyleWang, Manyi, Jiaxing Yang, Binghua Huang, Yuan Yang, and Yadong Xu. 2020. "Three-Dimensional Device-Free Localization for Vehicle" Sensors 20, no. 13: 3775. https://doi.org/10.3390/s20133775
APA StyleWang, M., Yang, J., Huang, B., Yang, Y., & Xu, Y. (2020). Three-Dimensional Device-Free Localization for Vehicle. Sensors, 20(13), 3775. https://doi.org/10.3390/s20133775