Detection of out-of-seam and out-of-scope mining using double filtering method

In China, small-scale coal mines, particularly those nearing depletion, are frequently affected by out-of-seam and out-of-scope mining, leading to significant safety hazards and frequent accidents. To overcome the limitations of existing detection methods, which are often time-consuming and inaccurate, we propose a novel approach that integrates Kalman and particle dual filtering techniques. This methodology employs handheld positioning and data acquisition devices, carried by law enforcement personnel during mine inspections. The system incorporates a strapdown inertial navigation unit, enhanced by Kalman filtering and a "zero-speed" correction mechanism, to deliver real-time navigation capabilities. As inspectors navigate the mine and pass through designated Bluetooth beacon zones, the particle filtering model processes the navigation data to correct errors dynamically. The resulting data, which includes the inspectors' positions and movement trajectories, is preprocessed and transmitted to a centralized ground server. This data is then analyzed using Simultaneous Localization and Mapping (SLAM) algorithms, and cross-validated against officially approved mine maps. This approach enables the precise and efficient identification of out-of-seam and out-of-scope mining activities, a capability validated through experimental trials.