The application of NLOS suppression method based on prior data-driven and acceleration-collaborative adaptive mechanism in mine rescue positioning
HU Qingsong
ZHONG Hui
CHENG Yuanxun
WANG Liudi
LI Shiyin
Abstract：In mine accident rescue operations,the underground environment becomes extremely complex.Obstructions such as tun-nels and rocks,coupled with changes in physical space and the presence of high-concentration gases,lead to attenuation,reflection,refraction,and the formation of multiple propagation paths of ultra-wide band signals.These factors make Non-Line-of-Sight(NLOS)and multipath effects very pronounced,which severely affects positioning accuracy.To address these issues,a novel al-gorithm based on a prior-data-driven Gaussian mixture model(GMM)and acceleration-collaborative adaptive extended Kalman fil-ter(PGA-AEKF)is proposed.The algorithm first uses the Akaike information criterion and the Bayesian information criterion to de-termine the number of Gaussian mixture distributions.It then employs particle swarm optimization and simulated annealing al-gorithms to optimize the expectation maximization algorithm to determine the parameters of each distribution.The improved GMM is used to fit the collected time-delay prior data distribution.Subsequently,the real-time NLOS probability value of the scene is ob-tained using geometric methods,and an adaptive factor is generated in combination with the results of abnormal acceleration judg-ments of pedestrian or vehicle motion.Finally,the adaptive factor is used to adjust the size of the noise covariance matrix to improve positioning accuracy.To verify the performance of the algorithm,trajectory tracking experiments were conducted in Yunlongshan Tunnel and underground space smoke environment.The results show that in the tunnel scenario,the proposed PGA-AEKF al-gorithm achieved a significant reduction in average error compared to pure inertial measurement unit algorithm,least squares meth-od,and extended Kalman filter algorithm,with reductions of approximately 86.67%,69.23%,and 38.46%,respectively.In the smoke scenario,the average error was also significantly reduced by approximately 83.78%,60.00%,and 25.00%compared to the aforemen-tioned algorithms.The study shows that the PGA-AEKF algorithm can effectively deal with NLOS interference,suppress noise and errors.
Keywords：mine accident rescuetarget localizationultra-wide bandGaussian mixture modelacceleration adaptive extended Kal-man filter
Publication Date：2025-11-20
Online Publishing Date：2025-11-25(First online date of this platform, not the publication date of the document)
Pages：10( 201-210 )