Improved Distributed Target Tracking Algorithm For Underground Coal Mines

We propose an improved distributed target tracking algorithm that combines linear fitting with Kalman filtering for coal mines. We establish dynamic cluster by the current position of moving targets, and cluster head nodes will process the latest observation data from cluster member nodes. Finally, we forecast the moving targets by combining linear fitting method and Kalman filtering algorithm, and take the predicting values obtained from the linear fitting and the predicted values from Kalman filtering as the real predictive values to obtain the state estimation of targets. Through such improvement, we can correct the predicted values timely. Simulation results show that the improved algorithm combined the advantages of two algorithms has a good tracking performance compared with the traditional distributed target tracking algorithm.