Research On Localization Algorithm Based On Ranging In Long And Narrow Space

With the development of coal mine industry,it also faces the pressure of safe production.Such a narrow and long space as coal mine roadway,the environment is complex and changeable,the working area is narrow,and the underground accident are inevitable.Accurate positioning technology is one of the most urgent technologies to improve the level of coal mine safety production.However,the existing underground precise positioning technology does not fully match the needs of coal mine safety production.Therefore,in view of the particularity of positioning in such a long and narrow space as underground roadway,the research on the accurate positioning method suitable for underground roadway has laid a solid foundation for improving the level of safety production,which is of great significance in theory and practice.In this paper,the underground roadway of coal mine is taken as the research background,and the research is carried out from two aspects of improving the quality of ranging information and improving the accuracy of position estimation,so as to reduce the positioning error in general.The underground electromagnetic wave signal is seriously interfered by multipath propagation and non-line-of-sight transmission,and the ranging accuracy is poor.In this paper,a denoising algorithm for ranging data based on deep learning is proposed.Through comparison and analysis,on the basis of eliminating synchronization error and timing error by using symmetric double sided-two way,hampel filtering method is used to remove discrete outliers in ranging data,and the ranging error is preliminarily reduced.Then,combined with the ELU activation function,the ELU-Dn CNN for denoising the ranging data is constructed,which achieves the purpose of significantly improving the ranging accuracy.Through the above research,accurate data is provided for subsequent position calculation,and the influence of ranging errors on positioning accuracy is weakened.The experimental results show that the proposed denoising algorithm has achieved obvious optimization results in terms of distance estimation accuracy and stability.Compared with other denoising algorithms,the proposed algorithm has more advantages.The existing positioning algorithms cannot be well applied to the underground roadway space.In this paper,an improved quadratic constrained weighted least squares algorithm based on ranging is proposed.The nonlinear nonconvex optimization problem is transformed into a convex optimization problem,which can effectively locate the target accurately.By introducing the measurement distance equation into the positioning model,this algorithm enhances the positioning information in the objective function in the final optimization formula,which improves the robustness of the positioning model.Then,the positioning accuracy is improved by using the constraint relationship between variables while obtaining the linearization method of the positioning model.Finally,the closed-form expression of position estimation is obtained by Lagrange multiplier method,which reduces the computational complexity.Simulation and experimental results show that the proposed algorithm still has high positioning accuracy in poor environments,low computational complexity and good robustness.There are 32 figures,5 tables,and 96 references in this thesis.