Landslide Monitoring Algorithm Based on Improved Untraced Kalman filter
Landslide collapse is a common problem during open-pit mining,and it is an important factor affecting the safety production of open-pit mine slopes.The study and treatment of open-pit mine slope stability is an indispensable component of mining technology work.Therefore,a new unscented Kalman filtering algorithm based on improved grey wolf algorithm optimization is proposed to solve the problems of poor robustness of unscented Kalman filtering to model uncertainty and easy loss of tracking ability to sudden changes when the system reaches a stationary state.The traditional grey wolf algorithm(GWO)is prone to problems such as local optima and slow convergence speed in the later stage.Therefore,a nonlinear control parameter combination adjustment strategy is proposed to form an improved grey wolf optimization algorithm.The improved grey wolf optimization algorithm is used for real-time optimization of the unscented Kalman filter.The results show that the proposed algorithm has small errors,high accuracy,and good predictive performance.
Mining slopeTrajectory predictionGrey Wolf Optimization AlgorithmKalman filtering
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