Aiming at the shortcomings of the existing indoor location algorithm, such as low accuracy of positioning, high deployment and maintenance cost, and unstable robustness, this paper proposes a method of indoor location based on the integration of smartphone with WiFi and magnetic field using multi-sensor fusion. In the initial stages of positioning, rough location is achieved by Wi-Fi-RSSI fingerprints which provides an initial location and geomagnetic matching area for indoor positioning based on particle filter magnetic field matching. This paper proposes the use of median filter algorithm to deal with the original magnetic field data and covariance interpolation algorithm to generate magnetic field map, and effectively reduce the interference which caused by geomagnetic fluctuations, thereby it will improves the positioning accuracy. Finally, through conducting comprehensive experiments and tests, the results show that the proposed technique can reliably achieve 0.836 meters precision in current experimental environment.
As the most people spend 70% to 90% of their time using mobile location-based services (LBS) in indoor environments, the need for indoor location technology is increasing and becoming critical. In recent years, with the rapid development of wireless communication technology and the widespread popularity of intelligent terminals, wireless location technology has also become a research focus. At present, the widely used global positioning system in the outdoor field can achieve relatively accurate positioning and navigation, but the GPS signal in the indoor serious degradation or even unavailable [
Modern architecture of reinforced concrete or steel frame structure on the indoor magnetic field disruption, the formation of a non-uniform magnetic field environment create the differences of the magnetic field in different tracks. Thus, using of the magnetic field in the indoor environment, unique, stability and the difference between different regions to achieve positioning become possible. Therefore, some teams began to explore the use of geomagnetism to achieve indoor positioning, which is more representative of the University of Oulu, Finland, Janne Haverinen et al. and the use of geomagnetism for indoor positioning. The research team has completed a verification test in a mine tunnel of about 1380 meters with a positioning accuracy of 1.5 meters [
In the office environment, the first set walking path in accordance with certain laws, and then experimental personnel holding a smartphone to walk at a constant speed in the set route, collecting and recording the magnetic field used to generate line magnetic filed map. Secondly, a reference point (RP) which corresponds to an absolute coordinate is used to assist the positioning of the target is set on the route. At the same time, the RSS of WiFi and the magnetic field are captured at each reference point for positioning. Mobile users in the environment has access to map data, the use of handheld terminals, real-time data acquisition and the use of existing algorithms to obtain real-time positioning results.
In this paper, the feasibility and data acquisition and processing of magnetic field for indoor location are introduced. As shown in
With the development of micro-electro-mechanical systems (MEMS), mobile intelligent terminals are embedded with sensors, such as magnetometers and the like for mobile users to use and magnetic field positioning, and it provides the possibility. In order to prove the stability of the geomagnetic signal, two sets of data are collected at different time periods in the same place. As shown in
Median filtering is essentially a method of determining the filter output response by minimizing the sum of the absolute values of the errors [
To the minimum, it must have
To satisfy the Formula (2), the value in the middle position after the size order should be taken. As shown in
Because of the characteristics of geomagnetic space fluctuation, the covariance interpolation algorithm is more effective than other interpolation algorithms. Kriging is a mathematical statistical method for spatial interpolation [
where
calculated every time, and then it fused to the global grid map. And the magnetic field predicted value
The use of smartphone that owns magnetometer to read the magnetic field data is relative to the phone’s coordinate system. In terms of the same position to change the attitude of the mobile phone, reading the magnetic field vector is different, while the smartphone carried by the magnetometer itself exists measuring noise, and the error is random. This also leads to the non-uniqueness of geomagnetic fingerprints in the region. The particle filter algorithm can solve this problem. Particle filter algorithm, including the following basic steps:
1) Initialization. The particle set
2) Particle update.
Which
3) Importance of resampling
Based on the Monte Carlo method, the objective function is fitted by random sampling. At the moment
4) Weight calculation.
Since each particle represents a possibility of the target state, the purpose of system observation to make the particle similar to the actual situation obtains a larger weight, and the particle with the larger difference from the actual situation gets a smaller weight. Consequently, the weighting of exponential distribution is calculated:
Weighted normalization:
5) Estimate the user location
The current location is estimated using the particles sampled in the previous step.
In order to verify the fingerprint model and proposed algorithm in this paper, an experimental building was chosen as the experimental environment. In the experiment, we select the Huawei honor-7 smartphone as a receiving device. TP- link dual-band router is as the WiFi access point to construct the WiFi fingerprint library and auxiliary magnetic matching positioning. The experimental scenarios were selected in the back hall corridor of the Science and Technology Building hall. In this area, four APs were deployed. In the corridor, according to the established route, setting a RP reference point every 0.5 meters, and recording the RP’s WiFi signal RSSI which generates fingerprints database. Then, on the same route, according to the uniform velocity motion, the geomagnetic field is collected, and the collected data is filtered through the median filter, mapped to the route uniformly. Finally, covariance interpolation is used to generate the geomagnetic map.
In the positioning phase, first, the use of WiFi fingerprints obtain the initial position and rough location of the delineation. In combination with the error of the RSSI localization algorithm in the experiment, we will delimit the rough position and then retrieve the magnetic filed data of the database in the coarse position and use the particle filter algorithm to match the geomagnetic data in the geomagnetic map, and the geomagnetic data is collected in the actual movement. Secondly, the use of mobile phones with accelerometer and electronic compass inferres the direction and displacement of the user movement. Subsequently, using the geomagnetism distribution in the coarse region, the particle filter algorithm is used to realize the accurate indoor location, and finally determine the moving trajectory of the user.
In the use of WiFi positioning phase, we use the commonly used location fingerprint method. In the known indoor area, n WiFi access points (AP1, AP2, AP3... APn) is deployed. Furthermore, the RSS values
As shown in
ing, K nearest neighbor algorithm is used. Instead of using the nearest distance as the location of the mobile terminal, the nearest K (
Here the distance is calculated using the Euclidean distance. In Equation (9),
According to the specific circumstances of the experimental scene, the next phase of the magnetic filed data generated by the map is shown in
In this paper, the particle filter algorithm for magnetic matching positioning can use the location of the results of geomagnetic matching to provide a search range. At the same time, the geomagnetic data collected online includes the location information at this moment, (magnetic field data and direction angle, which the online data and database map to match.) The resulting positioning of the trajectory is shown in
The error analysis of WiFi-RSSI and geomagnetic field in
to the location of WiFi-RSSI, the convergence of the particle filter is obviously improved, and the run-time is saved, and the mean of positioning error is 0.836 meters. Because the measured value is disturbed by the surrounding environment, the effect of the whole algorithm is in a fluctuating state. The accuracy and stability of the algorithm are determined by the RSSI algorithm that was originally used. At the same time, the whole consideration is used in the algorithm, instead of judging the position of one or several data, so the degree of susceptibility to interference is better than that of wifi.
In order to solve the problem which is high mismatching rate in the process of matching the location of the magnetic field, this paper proposes a fusion location algorithm of WiFi aid magnetic field, which is simple and low cost, and it combines with particle filter algorithm to achieve ideal the positioning effect. The experimental results show that this method has potential advantages in improving the accuracy and real-time performance. Future work will continue to optimize the algorithm and research to improve positioning accuracy and timeliness.
This work is suported by the National Natural Science Foundation of China (No.61371107), the Guangxi Experiment Center of Information Science (No.LD16061X), the Guangxi Natural Science Foundation (No.2016GXNSFBA38014), and the China Postdoctoral Science Foundation (No.2016M602921XB), the Innovation Project of GUET Graduate Education (YJCXS201517).
Wang, E.L., Wang, M., Meng, Z.B. and Xu, X.Y. (2017) A Study of WiFi-Aided Magnetic Matching Indoor Positioning Algorithm. Journal of Computer and Communications, 5, 91-101. https://doi.org/10.4236/jcc.2017.53011