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The magnetic information measured on the smartphone platform has a large fluctuation and the research of indoor localization algorithm based on smart-phone platform is less. Indoor localization algorithm on smartphone platform based on particle filter is studied. Robust local weighted regression is used to smooth the original magnetic data in the process of constructing magnetic map. Use moving average filtering model to filter the online magnetic observation data in positioning process. Compare processed online magnetic data with processed magnetic map collected by smartphone platform and the average matching error is 0.3941uT. Average positioning error is 0.229 meter when using processed online and map data.

Localization system has become an indispensable tool in life and work, we usually use GPS or Beidou to complete the localization function in outdoor environment, but when entering the indoor environment, the GPS signal become weakened and even unable to achieve positioning function. Based on people’s demand for indoor localization services, indoor localization technology in recent years has been a great development. [

Localization technology proposed in the past mainly based on ZigBee, Bluetooth, ultrasound, geomagnetism, UWB, RFID, WiFi. Indoor localization algorithms based on ZigBee, Bluetooth [

In the past, magnetic indoor localization algorithm mostly concentrated on the particle filter algorithm [

The robust local weighted regression algorithm which is proposed by Cleveland [

Percentage

In this paper,

(1) For each magnetic data

(2) Define the bisquare weight function as below:

Let

(3) Compute new

(4) Repeatedly carry out steps 2 and 3 for T times, the final

The moving average filtering model gets the filtered result

where

Particle Filter algorithm is usually utilized in indoor magnetic localization to estimate the position of user. We use particles

we can get approximation probability

The motion model is

Experiments are conducted on the 3rd floor of keji building where is 27 meters long and 7 meters wide in Guilin University of Electronic Technology. The experimental environment is shown in

In the process of smoothing, numbers of iterations

netic data for there is not available empirical value. So we only discuss the size of window

Magnetic data of 5 meters long are used to simulation. From the simulation results, when

As the indoor localization has high demand of real-time, so in the process of filtering magnetic data, both filtering time and data stability have to be considered. Limited by experimental phone’s sampling frequency 100 HZ and the real-time is difficult to present by simulation, so the algorithm is only validated by simulation and there is no analysis about the number of moving average points. Set average number

From the simulation results, filtered data have great improvements compared to the original data in stability. Although there are still fluctuations, taking into account the real-time factors, these fluctuations can be acceptable.

Construct magnetic map by robust local weighted regression algorithm and compare the map with online magnetic observation processed by filter model to validate the two methods.

Through the above process, we get the magnetic map and online observation data which are used to simulate on platform MATLAB. The step length in walking and simulation are set the same of 0.5 meter, the variance of the weight func-

tion is

After the particles converge, the positioning results are analyzed as follows. Simulation result by unprocessed data is lower in stability and accuracy than the processed data. The average error using processed data is 0.229 meter compared to the unprocessed data’s 0.394. 96.9% of the localization errors are lower than 0.5 meter by processed data but unprocessed data’s 77.3%.

In this paper, robust local weighted regression algorithm is used to smooth the magnetic original data. Online magnetic observation data are filtered by moving average filter. Compare processed magnetic map and online processed magnetic observation, the average error of data matching is 0.3941uT. Indoor localization based on particle filter using processed map and online observation shows ave-

rage positioning error of 0.229 meter which is relatively better. But the problem is also exists, this result is in experimental environment. In the procedure of map construction and online data obtained, the height and direction of smartphone are same. We have to solve these problems by adding other algorithm such as magnetic sensor calibration algorithm or using magnetic variation as observation.

This work is supported 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).

Meng, Z.B., Wang, M., Wang, E.L. and Xu, X.Y. (2017) Robust Local Weighted Regression for Magnetic Map-Based Localization on Smartphone Platform. Journal of Computer and Communications, 5, 80-90. https://doi.org/10.4236/jcc.2017.53010