Abstract
The access points (APs) in a coal mine wireless local area network (WLAN) are generally sparsely distributed. It can, with difficulty, satisfy the basic requirements of the fingerprint positioning based on Wi-Fi. Currently, the effectiveness of positioning is ensured by deploying more APs in an underground tunnel, which significantly increases system cost. This problem can be solved by using the Virtual Access Point (VAP) method that introduces virtual access points (VAPs), which can be virtually arranged in any part of the positioning area without installing actual access points. The drawback of the VAP method is that the generated received signal strength (RSS) value of a VAP is calculated based on the mapping of RSS value from only one corresponding access point (AP). This drawback does not consider the correlation between different AP signals and the generated RSS value of a VAP, which makes the modeling of fingerprint samples and real-time RSS collection incomplete. This study proposed a Multi-Association Virtual Access Point (MA-VAP) method takes into account the influence of multi-association. The multi-association coefficient is calculated based on the correlation between the RSS values of a VAP and multiple access points (APs). Then, the RSS value generated by a VAP is calculated using the multi-association function. The real-time collected RSS values from multiple APs related to this VAP are the input of the multi-association function. The influence of the number of VAPs and their arrangement on positioning accuracy is also analyzed. The experimental positioning results show that the proposed MA-VAP method achieves better positioning performance than the VAP method for the same VAP arrangement. Combined with the Weight K-Nearest Neighbors (WKNN) algorithm and Kernel Principal Component Analysis (KPCA) algorithm, the positioning error of the MA-VAP method of the error distance cumulative distribution function (CDF) at 90% is 4.5 m (with WKNN) and 3.5 m (with KPCA) in the environment with non-line-of-sight (NLOS) interference, and the positioning accuracy is improved by 10% (with WKNN) and 22.2% (with KPCA) compared with the VAP method. The MA-VAP method not only effectively solves the fingerprint positioning problem when APs are sparse deployed, but also improves the positioning accuracy.
Highlights
Underground personnel positioning is crucial for coal mine safety management
After that Kalman Filter (KF) [17] and Particle Filter (PF) [18] are used to correct the received signal strength (RSS) value generated by the Virtual Access Point (VAP)
In the on-line phase, even if the RSS values generated by VAPs in R1 and R2 was relatively stable after KF and PF, there were some errors when doing the optimal fingerprint matching with fluctuated fingerprint samples of reference point (RP) in R1 and R2
Summary
Underground personnel positioning is crucial for coal mine safety management. At present, Radio Frequency Identification (RFID) [1], ZigBee [2] and Wi-Fi [3,4] are the main technologies for underground personnel positioning. To improve the fingerprint positioning accuracy when APs sparse distribution, Zhang phase decomposition method to obtain the phase of multi-path by an AP and uses the decomposed phase as a fingerprint after the feature exaction by Principal Component Analysis (PCA). When RSS values from different APs are simultaneously collected at the same reference point (RP) without using VAPs, there is a certain correlation between the collected RSS values This correlation is not considered completely in the one-on-one association mapping calculation. Multi-Association Virtual Access Point (MA-VAP) method to improve the calculation precision of relationships between a VAP and APs. In the MA-VAP method, RSS values collected from a VAP and APs at each RP are considered to be measured data in the calculation of relationships between a VAP and APs. The paper analyzes the influence of the number and deployed location of VAPs on positioning accuracy.
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