Abstract
Aiming at the poor location accuracy caused by the harsh and complex underground environment, long strip roadway, limited wireless transmission and sparse anchor nodes, an underground location algorithm based on random forest and compensation for environmental factors was proposed. Firstly, the underground wireless access point (AP) network model and tunnel environment were analyzed, and the fingerprint location algorithm was built. And then the Received Signal Strength (RSS) was analyzed by Kalman Filter algorithm in the offline sampling and real-time positioning stage. Meanwhile, the target speed constraint condition was introduced to reduce the error caused by environmental factors. The experimental results show that the proposed algorithm solves the problem of insufficient location accuracy and large fluctuation affected by environment when the anchor nodes are sparse. At the same time, the average location accuracy reaches three meters, which can satisfy the application of underground rescue, activity track playback, disaster monitoring and positioning. It has high application value in complex underground environment.
Highlights
Introduction and problem statementSafety has always been a hot issue in coal mines, which accounts for 70% of the energy structure
This paper proposes a new underground location algorithm based on random forest and environmental factors compensation
The idea of fingerprint location algorithm based on random forest as follows: Firstly, the sampled signal is processed by Kalman filter to form fingerprint database; the feature data of the current positioning target is obtained in real time, and random forest is used for random forest prediction after Kalman filtering processing; the location information of unknown nodes is obtained
Summary
Safety has always been a hot issue in coal mines, which accounts for 70% of the energy structure. Underground location algorithm based on random forest and environmental factor compensation and variable. By establishing dual WiFi channel and signal transmission-reception timing mode, Sun Jiping et al proposed TOA underground target location method based on time error suppression (Sun and Li 2014). By analyzing the transmission loss model of roadways and dynamically obtaining the path fading index, Han Dongsheng et al proposed a weighted centroid location algorithm based on RSSI (Han et al 2013). This paper proposes a new underground location algorithm based on random forest and environmental factors compensation. It aims to solve the problem of insufficient location accuracy and large fluctuation affected by environment when the anchor nodes are sparse, and provides a reference for the application of high-precision location in the future
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