Abstract
Indoor localization of smartphones has received much attention recently and the smartphone localization is essential to a wide range of applications in office buildings, nursing homes, parking lots, and other public places. Existing solutions relying on inertial sensors or received signal strength suffer from large location errors and poor stability. We observe an opportunity in the recent trend of increasing numbers of wireless transmitters installed in indoor spaces to design a precise and robust indoor localization solution. We can extract fine-grained channel state information from wireless transmitters for indoor fingerprint localization. However, the accuracy of localization relying on a single physical quantity is limited and difficult to self-correct. This study proposes an integrated channel state information (CSI) and magnetic field strength (MFS) localization method (CSMS) that achieves sub-meter accuracy for smartphones. CSMS constructs an integrated fingerprint map of CSI and MFS and proposes the Local Dynamic Time Warping algorithm for geomagnetic tracking and the Multi-Module Data k-Nearest Neighbor algorithm for fusion fingerprint dynamic weighted comparison. By doing so, CSMS outputs enhanced accuracy with low cost, while overcoming the respective drawbacks of each individual sub-system. We conduct extensive experiments in two scenarios to validate the performance of CSMS. The results of experimental show that the mean distance error in both scenarios is less than 0.5m which is significantly superior to existing smartphone-based indoor positioning methods.
Highlights
Accurate indoor localization is a key enabler for many applications on the horizon, such as positioning and navigation in parking lots, real-time monitoring of the elderly in nursing homes, and hazard warnings in construction sites
In order to integrate channel state information (CSI) and magnetic field strength (MFS) more organically, we proposed Local Dynamic Time Warping (L-DTW) algorithm to match geomagnetic waveform for tracking and applied dynamic weights to CSI data of multiple Access Points (APs) according to the tracking results and reduced the localization range
We conduct extensive experiments in multiple scenarios to validate the performance of CSMS
Summary
Accurate indoor localization is a key enabler for many applications on the horizon, such as positioning and navigation in parking lots, real-time monitoring of the elderly in nursing homes, and hazard warnings in construction sites. We believe that CSI extracted from multiple Wi-Fi signals can be utilized together with magnetic field strength as fingerprint for localization to improve accuracy. In order to integrate CSI and MFS more organically, we proposed Local Dynamic Time Warping (L-DTW) algorithm to match geomagnetic waveform for tracking and applied dynamic weights to CSI data of multiple APs according to the tracking results and reduced the localization range. A. ARCHITECTURE This study proposes CSMS, an indoor localization method integrating CSI and geomagnetic field strength (GMFS). According to the tracking location, utilize M-KNN algorithm to dynamically weight multi-module data and narrow the positioning range for fingerprint comparison to obtain more accurate localization results. The steps of M-KNN are described in detail in algorithm.
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