Research on a wireless positioning algorithm for underground personnel

For problems that traditional underground fingerprint positioning algorithm needs to collect a large number of fingerprint data and positioning accuracy is not high, a wireless positioning algorithm for underground personnel based on differential evolution and artificial fish swarm algorithm optimization least square support vector machine (DEAFSA-LSSVM) was proposed. Firstly, the underground experimental area is divided into several small areas, and the fingerprint database is established by Kriging interpolation algorithm. Secondly, the hybrid intelligent algorithm of differential evolution and artificial fish swarm is used to optimize regularization parameters and width of kernel function, and the least squares support vector machine algorithm model is established. The wireless acquisition and reception terminal is used to collect wireless information data of undetermined site, and its small area is calculated by the least squares support vector machine algorithm model. Finally, the wireless information data in the small area is used for real-time positioning by weighted K-nearest neighbor algorithm. The experimental results show that the algorithm has high convergence speed and high classification accuracy, the classification accuracy is 98.87%; and has high positioning accuracy, the average positioning error is 1.51 m, which is 18.82% higher than that of the least squares support vector machine algorithm without optimization.