Underground Coal Mine Fingerprint Positioning Based on the MA-VAP Method

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.

Self-Driving Algorithm and Location Estimation Method for Small Environmental Monitoring Robot in Underground Mines

“…Recently, various studies on information communication technology (ICT)-based underground mine safety management systems are being conducted. There have been studies to prevent collisions between workers and equipment using Bluetooth beacons [6][7][8][9], radio-frequency identification (RFID) [10,11], and wireless access points (APs) [12,13] in an underground mine environment. In addition, there have been studies to measure environmental factors in underground mines using open-source hardware such as Arduino [14][15][16][17] and Raspberry Pi [18,19].…”

Application of a WiFi/Geomagnetic Combined Positioning Method in a Single Access Point Environment

“…Aiming at the sparse distribution of underground wireless access points, Song and Qian put forward an improved positioning method of virtual wireless access points in their paper [6]. This method can solve the weak correlation problem of the received signal strength collected by traditional virtual wireless access points and improve the accuracy of positioning.…”