Tunnel personnel positioning method based on TOA and modified location-fingerprint positioning

To position personnel in mines, the study discussed in this paper built on the tunnel personnel positioning method on the basis of both TOA and location-finger print (LFP) positioning. Given non-line of sight (NLOS) time delay in signal transmission caused by facilities and equipment shielding in tunnels and TOA measurement errors in both LFP database data and real-time data, this paper puts forth a database data de-noising algorithm based on distance threshold limitation and modified mean filtering (MMF), as well as a real-time data suppression algorithm based on speed threshold limitation and MMF. On this basis, a nearest neighboring data matching algorithm based on historical location and the speed threshold limitation is used to estimate personnel location and realize accurate personnel positioning. The results from both simulation and the experiment suggest that: compared with the basic LFP positioning method and the method that only suppresses real-time data error, the tunnel personnel positioning methods based on TOA and modified LFP positioning permits effectively eliminating error in TOA measurement, making the measured data close to the true positional data, and dropping the positioning error: the maximal positioning error in measurements from experiment drops by 9 and 3 m, respectively, and the positioning accuracy of 3 m is achievable in the condition used in the experiment.