UWB Anchor Node Optimization Deployment Using MGWO for the Coal Mine Working Face End

• Published in: IEEE transactions on instrumentation and measurement Vol. 72; p. 1
• Main Authors: Cao, Bo, Wang, Shibo, Ge, Shirong, Chen, Feng, Zhang, Hua
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation algorithms; coal mine working face; Coal mines; Coal mining; Configurations; deployment scheme; Dilution; Distance measurement; Faces; Geometry; Layout; Location awareness; Mathematical analysis; modified grey wolf optimizer; Optimization; Placement; Polynomials; Shape; Ultra-wideband; Ultrawideband; weight position dilution of precision; Work face
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2023.3250304

Appropriate deployment of anchor nodes (ANs) is an essential prerequisite for achieving high-precision positioning. To ameliorate the comprehensive positioning performance, the weight position dilution of precision (WPDOP), that is utilized to evaluate the appropriateness of the ANs placement geometry is proposed by taking into account the different error variances for each AN. A simple closed-form calculation WPDOP method is theoretically derived for UWB positioning system by utilizing elementary symmetric polynomials and Newton's identity, which is applied to evaluate the Ultra-wideband (UWB) deployment at the end of coal mine working face (CMWF). Several basic ANs layout strategies, based on the projection shape in the x-y plane, are designed for the end of CMWF, and the modified grey wolf optimizer (MGWO) algorithm is executed to optimize the ANs deployment scheme in order to attain the better configuration and higher positioning accuracy. Simulation and experiments verification are simultaneously performed via the UWB positioning system. The experimental results sufficiently demonstrate that MGWO deployment scheme offers lower average WPDOP values than the obtained ones when applying random placement, and the lowest average improvement percentage was approximately equal to 37.6%. Furthermore, after implementing the MGWO algorithm, the V-shape deployment configuration is capable to provide the lowest average WPDOP values and outperforms the other arrangements, keeping the lowest localization error. This is regarded as the best deployment and it gives significant guidance for the ANs sensor deployment at the end of CMWF.