世界有色金属Issue(9)：232-234,3.
基于改进无迹卡尔曼滤波的滑坡监测算法
Landslide Monitoring Algorithm Based on Improved Untraced Kalman filter
祁闻 1岳鹏 1卢晓辉 1庞哲铭2
作者信息
- 1. 鞍钢集团矿业弓长岭有限公司露采分公司,辽宁 辽阳 111008
- 2. 辽宁工业大学电子与信息工程学院,辽宁 锦州 121001
- 折叠
摘要
Abstract
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.关键词
矿山边坡/轨迹预测/灰狼优化算法/卡尔曼滤波Key words
Mining slope/Trajectory prediction/Grey Wolf Optimization Algorithm/Kalman filtering分类
天文与地球科学引用本文复制引用
祁闻,岳鹏,卢晓辉,庞哲铭..基于改进无迹卡尔曼滤波的滑坡监测算法[J].世界有色金属,2024,(9):232-234,3.
