Coal Mine Underground Positioning Algorithm based on RSSI Model Correction and Node Cooperation

To address the issues of environmental complexity and low positioning accuracy faced by coal mine underground positioning systems, an improved localisation algorithm based on Received Signal Strength Indication (RSSI) model correction and node collaboration, namely, the RSSI-MCNC (RSSI Model Correction and Node Collaboration) algorithm, is proposed. First, this algorithm employs Kalman filter technology to optimise the collected RSSI values, improving signal stability and range model accuracy. Second, more precise ranging results are achieved by dynamically adjusting the RSSI model parameters to adapt to changes in mining environments. In the localisation stage, the localised unknown nodes are used as cooperative nodes to position other unknown nodes and solve the objective function through the improved weighted centroid algorithm and gradient descent method, precisely locating the unknown nodes. The simulation results indicate that the RSSI-MCNC algorithm can significantly improve the positioning coverage and accuracy of fixed anchor nodes and the random distribution of unknown nodes in mine roadways, especially in the case of limited anchor nodes. This is significant for improving the safety of mine personnel and equipment.