Method of multi-personnel fingerprint positioning in mine tunnel based on Optimized Spectral Clustering and cross-tagging

Centralized positioning system has been widely used in mining localization, but it shows a low accuracy and efficiency compared to distributed method.Because of the influential position in mining positioning, the authors analyze the necessity and algorithm requirements of the distributed mine positioning system modeling.This model is based on a determined algorithm in machine learning called Optimized Spectral Clustering (OSC) which is developed from Spectral Clustering (SC) introduced in the paper.The OSC comes from traditional SC, but has been improved using Chebyshev estimation to simplify the calculation process in SC, and brings a high efficiency that traditional SC cannot reach.In the period of location fingerprint gathering, the authors put forward a new method called cross-tagging method for multi-entities tracking.Combined with OSC, a whole localization system is set up in the study and these two techniques bring a higher efficiency and accuracy in mining localization.