Abstract
This paper presents a method that trains the WiFi fingerprint database using sensor-based navigation solutions. Since micro-electromechanical systems (MEMS) sensors provide only a short-term accuracy but suffer from the accuracy degradation with time, we restrict the time length of available indoor navigation trajectories, and conduct post-processing to improve the sensor-based navigation solution. Different middle-term navigation trajectories that move in and out of an indoor area are combined to make up the database. Furthermore, we evaluate the effect of WiFi database shifts on WiFi fingerprinting using the database generated by the proposed method. Results show that the fingerprinting errors will not increase linearly according to database (DB) errors in smartphone-based WiFi fingerprinting applications.
Highlights
Mobile location-based services (LBS) are attracting the attention of many mobile device companies due to their potential applications in a wide range of personalized services [1]
Walking tests were conducted in two indoor environments: the Energy, Environment, and Experiential learning (EEEL) building, in which the average weighted access points (APs) number at one point was over 15; and the Engineer building (ENB), in which the average weighted
Even though our attitude estimation algorithm can dealing with different scenarios such as handheld, ear, dangling, pocket, and backpack [61], we conducted the tests in this paper with only handheld mode to focus on investigating WiFi positioning
Summary
Mobile location-based services (LBS) are attracting the attention of many mobile device companies due to their potential applications in a wide range of personalized services [1]. The research [38] estimates the location of WiFi APs or other radio beacons using pedestrian dead-reckoning with high-quality foot-mounted IMUs, while [34,39,40,41] propose similar systems or approaches using handheld smartphones Based on this idea, it is possible for mobile users to collect WiFi fingerprints automatically in daily life by conducting sensor-based navigation. We propose an approach that utilizes similar ideas as [34,39,40], i.e., training the WiFi. DB using the navigation data from the users, and utilizing different strategies to control sensor-based navigation errors and in turn control the drifts of the generated WiFi DBs. First, we use a strategy that combines different navigation trajectories that move in and out of a building to make up the DB; we restrict the time length of available indoor navigation trajectories, i.e., the trajectories from the last epoch that receives GNSS signal before entering an indoor area to the first epoch that receives GNSS signal after walking out of the indoor area.
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