An Improved Particle Filter Algorithm for Geomagnetic Indoor Positioning

Abstract

Geomagnetic indoor positioning is an attractive indoor positioning technology due to its infrastructure-free feature. In the matching algorithm for geomagnetic indoor localization, the particle filter has been the most widely used. The algorithm however often suffers filtering divergence when there is continuous variation of the indoor magnetic distribution. The resampling step in the process of implementation would make the situation even worse, which directly lead to the loss of indoor positioning solution. Aiming at this problem, we have proposed an improved particle filter algorithm based on initial positioning error constraint, inspired by the Hausdorff distance measurement point set matching theory. Since the operating range of the particle filter cannot exceed the magnitude of the initial positioning error, it avoids the adverse effect of sampling particles with the same magnetic intensity but away from the target during the iteration process on the positioning system. The effectiveness and reliability of the improved algorithm are verified by experiments.

Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter

“…The experiment results show that the average position estimation error is 1.72 m and the RMSE is 1.89 m, the positioning accuracy and stability are improved. Contrary to the high computational burden caused by increasing the number of particles in papers [26,28,29], the size of the particle set in this paper is 100 and the computational load is lower compared with them. Furthermore, the single-point-based magnetic positioning (SPMP) method is more flexible and easier to be implemented and its computational load is also not very high.…”

“…But the particle filter algorithm makes up for this shortcoming and has high positioning accuracy, which is also a typical single-point-based fingerprint positioning algorithm [24,25]. In related research, Huang et al [26] used the Hausdorff distance and Pearson correlation coefficient [27] to control the initial position error and accelerate the convergence speed of the filter. In [28], an adaptive particle filter algorithm was proposed in geomagnetic positioning.…”

Qualitative study of the incompatibility of indoor map file formats with location software applications

“…Our data sampling method was to include only that software that was commercially available at the time of our study. Applications were excluded from our evaluation if they were in research stages only at the time of writing, such as crowd-sourcing [10] or geo-magnetic positioning [6]. Further, ESRI's ArcGIS Indoors was excluded from analysis because it is only in beta and not commercially available.…”

Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients K-Nearest Neighbor

“…With the speedy development of mobile communication technology and the Internet of Things, indoor positioning and localization technology have attracted widespread attention as an essential part of location-based services. In recent years, researchers have proposed some indoor positioning and localization methods based on different technologies, such as Ultra-wideband [ 1 , 2 ] (UWB), Bluetooth [ 3 , 4 , 5 ], Wireless Fidelity [ 6 , 7 , 8 ] (Wi-Fi), Radio Frequency IDentification [ 9 , 10 ] (RFID), and magnetic field [ 11 , 12 , 13 ] (MF). Among them, the MF, Wi-Fi, and Bluetooth technologies have some advantages (such as easy deployment, low cost), which provide many applications in diverse fields, such as asset tracking, patient monitoring, warehouse management, and crowd analysis, etc.…”