Multi-Stage Pedestrian Positioning Using Filtered WiFi Scanner Data in an Urban Road Environment

Since widespread applications of wireless sensors networks, low-speed traffic positioning based on the received signal strength indicator (RSSI) from personal devices with WiFi broadcasts has attracted considerable attention. This study presents a new range-based localization method for outdoor pede...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-06, Vol.20 (11), p.3259
Main Authors: Huang, Zilin, Xu, Lunhui, Lin, Yongjie
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
distance estimation
Euclidean geometry
Experiments
Global positioning systems
GPS
Kalman filters
Localization
Localization method
Low speed
Methods
pedestrian positioning
piecewise polynomial regression
Polynomials
Propagation
Real time
Sensors
Series expansion
Signal strength
Smartphones
Software
Statistical analysis
Taylor series
Traffic control
Traffic flow
Traffic speed
WiFi detector
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Summary:Since widespread applications of wireless sensors networks, low-speed traffic positioning based on the received signal strength indicator (RSSI) from personal devices with WiFi broadcasts has attracted considerable attention. This study presents a new range-based localization method for outdoor pedestrian positioning by using the combination of offline RSSI distance estimation and real-time continuous position fitting, which can achieve high-position accuracy in the urban road environment. At the offline stage, the piecewise polynomial regression model (PPRM) is proposed to formulate the Euclidean distance between the targets and WiFi scanners by replacing the common propagation model (PM). The online stage includes three procedures. Firstly, a constant velocity Kalman filter (CVKF) is developed to smooth the real-time RSSI time series and estimate the target-detector distance. Then, a least squares Taylor series expansion (LS-TSE) is developed to calculate the actual 2-dimensional coordinate with the replacement of existing trilateral localization. Thirdly, a trajectory-based technique of the unscented Kalman filter (UKF) is introduced to smooth estimated positioning points. In tests that used field scenarios from Guangzhou, China, the experiments demonstrate that the combined CVKF and PPRM can achieve the highly accurate distance estimator of
ISSN:1424-8220
1424-8220
DOI:10.3390/s20113259