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.

Enhanced Fingerprinting Based Indoor Positioning Using Machine Learning

Due to the inability of the Global Positioning System(GPS)signals to penetrate through surfaces like roofs,walls,and other objects in indoor environments,numerous alternative methods for user positioning have been presented.Amongst those,the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems(IPS)as the need for lineof-sight measurements is minimal,and it achieves better efficiency in even complex indoor environments.Offline and online are the two phases of the fingerprinting method.Many researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a concern.Machine learning is used for the model training in the offline phase while the locations are estimated in the online phase.Many researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an issue.Machine learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization,creating precise models to predict an indoor location.Large training sets are a key for obtaining better results in machine learning problems.Therefore,an existing WLAN fingerprinting-based multistory building location database has been used with 21049 samples including 19938 training and 1111 testing samples.The proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms(kNN,WkNN,FSkNN,and SVM)for estimating the location.The proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84%as compared to all variants of the proposed method for 108703 m^(2) area.

Aerodynamic Performance of a Flapping Foil with Asymmetric Heaving Motion near a Wall

The effect of asymmetric heaving motion on the aerodynamic performance of a two-dimensional flapping foil near a wall is studied numerically. The foil executes the heaving and pitching motion simultaneously. When the heaving motion is symmetric, the mean thrust coefficient monotonically increases with the decrease in mean distance between foil and wall. Meanwhile, the mean lift coefficient first increases and then decreases sharply. In addition, the negative mean lift coefficient appears when the foil is very close to the wall. After the introduction of asymmetric heaving motion, the influence of wall effect on the force behavior becomes complicated. The mean thrust coefficient is enhanced when the duration of upstroke is reduced. Moreover, more and more enhancement can be achieved when the foil approaches the wall gradually. On the other hand, the positive mean lift coefficient can be observed when the duration of downstroke is shortened. By checking the flow patterns around the foil, it is shown that the interaction between the vortex shed from the foil and the wall can greatly modify the pressure distribution along the foil surface. The results obtained here might be utilized to optimize the kinematics of the Micro Aerial Vehicles （MAVs） flying near a solid wall.