Volume 21 Issue 2
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2021  >  21(2): 243-255
LIN Yong-jie, HUANG Zi-lin, WU Pan, XU Lun-hui. RSSI positioning method of vehicles in tunnels based on semi-supervised extreme learning machine[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 243-255. doi: 10.19818/j.cnki.1671-1637.2021.02.021
Citation: LIN Yong-jie, HUANG Zi-lin, WU Pan, XU Lun-hui. RSSI positioning method of vehicles in tunnels based on semi-supervised extreme learning machine[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 243-255. doi: 10.19818/j.cnki.1671-1637.2021.02.021
shu

RSSI positioning method of vehicles in tunnels based on semi-supervised extreme learning machine

doi: 10.19818/j.cnki.1671-1637.2021.02.021

  • School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, Guangdong, China

Funds:

National Natural Science Foundation of China 61903145

Science and Technology Planning Project of Guangzhou 201803030045

Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province pdjh2020a0030

More Information
  • Author Bio:

    LIN Yong-jie(1987-), male, associate professor, PhD, linyjscut@scut.edu.cn

  • Corresponding author: HUANG Zi-lin(1995-), male, graduate student, ctjulian@mail.scut.edu.cn
  • Received Date: 2020-12-09
  • Publish Date: 2021-04-01
    Abstract
  • To improve the identification efficiency of highway tunnel emergencies and to realize the full-time monitoring of road traffic conditions, the problem of positioning connected automated vehicles based on the received signal strength indicator (RSSI) was studied based on a ubiquitous wireless sensor network on an intelligent road. Considering the continuous motion characteristics of vehicles in a tunnel, a semi-supervised extreme learning machine (SSELM) with a locally linear embedding (LLE) algorithm was proposed to achieve the RSSI fingerprint positioning. In the offline phase, the dimension reductions for a few RSSI sample datasets with their vehicle positions marked and for a mass of unmarked ones were conducted by using the LLE, and the low-dimensional manifolds corresponding to their high-dimensional data, which represented the target's location information, were recognized. The mapping relationship between the RSSI data and vehicle positions was fitted based on the SSELM. In the online phase, real-time collected RSSI data after manifold dimensionality reduction were put into the calibrated SSELM to estimate the positions of vehicles. The estimated position was smoothed using an unscented Kalman filter (UKF). Analysis result shows that compared with the existing semi-supervised learning algorithms, the proposed method can achieve better positioning performance regardless of the vehicle travel speed and deployed distances. Under a change in key variables, such as the proportion of marked data (reduced by 50%-90%), number of unmarked data (0-1 000), and deployed sensor distance (10-25 m), the proposed method still has the best positioning performance with a minimum average error of 3.09 m. In terms of computational complexity, when the marked data comprise 30% of the dataset (only 96 reference points), the average positioning error is 3.8 m and the training time reduces to 8.7 s. Therefore, the proposed SSELM with LLE algorithm can provide promising positioning performance for vehicles with different driving speeds in an environment with sparsely or densely deployed sensors. In addition, it has a shorter training time and lower dependence on sample size, which makes it an effective method for the auxiliary positioning of connected automated vehicles in tunnels. 2 tabs, 11 figs, 34 refs.

     

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