Improved Kalman Filter-Based Weighted Center-of-Mass Localization Algorithm

In wireless sensor networks, for the problem that the received signal strength indication (RSSI) is affected by signal noise interference in the mining environment and has a large deviation in distance measurement accuracy, this paper proposes an improved weighted center-of-mass localization algorithm based on Kalman filtering. The collected RSSI signal values are corrected using the Kalman filter to mitigate the effects caused by the environment to a certain extent. The environmental parameters in the ranging equation were optimized using linear regression, and the weighted location of the point to be measured was calculated by calculating the sum of the squares of the ratio of the radius distance between the wireless access nodes and the RSSI value to improve the weighted center-of-mass positioning algorithm. The experiments show that the improved algorithm has significantly reduced the positioning error compared with the original positioning algorithm, and can basically meet the positioning requirements of underground coal mines.