Abstract
For smartphone indoor localization, an INS/WiFi hybrid localization system is proposed in this paper. Acceleration and angular velocity are used to estimate step lengths and headings. The problem with INS is that positioning errors grow with time. Using radio signal strength as a fingerprint is a widely used technology. The main problem with fingerprint matching is mismatching due to noise. Taking into account the different shortcomings and advantages, inertial sensors and WiFi from smartphones are integrated into indoor positioning. For a hybrid localization system, pre-processing techniques are used to enhance the WiFi signal quality. An inertial navigation system limits the range of WiFi matching. A Multi-dimensional Dynamic Time Warping (MDTW) is proposed to calculate the distance between the measured signals and the fingerprint in the database. A MDTW-based weighted least squares (WLS) is proposed for fusing multiple fingerprint localization results to improve positioning accuracy and robustness. Using four modes (calling, dangling, handheld and pocket), we carried out walking experiments in a corridor, a study room and a library stack room. Experimental results show that average localization accuracy for the hybrid system is about 2.03 m.
Highlights
With the increasing of smartphones, there is an increasing demand for location-based services, such as fire rescue, pedestrian tracking, advertising, and marketing
To deal with multi-dimensional signals, we propose a Multi-dimensional Dynamic Time Warping (MDTW) method based on the traditional Dynamic Time Warping (DTW)
Considering the relationship between the MDTW distance and positioning error, we propose a MDTW-based weighted least squares (WLS) to reduce position error and improve robustness
Summary
With the increasing of smartphones, there is an increasing demand for location-based services, such as fire rescue, pedestrian tracking, advertising, and marketing. According to the relationship between the position of a user and the Received Signal Strength (RSS), the WiFi attenuation model is used to estimate the location of a Sensors 2018, 18, 1458; doi:10.3390/s18051458 www.mdpi.com/journal/sensors. For complex indoor environments, many objects (e.g., walls , ground, obstacles and users) cause multipath effects [11] It is difficult for the attenuation model to provide reliable indoor positioning accuracy. The WiFi fingerprint matching model is another technique for estimating the location of a user. Signals from different WiFi chips and changes in the indoor environment lead to fingerprint mismatches. To improve indoor positioning accuracy and robustness, we propose an INS/WiFi indoor localization system using a smartphone. The wireless signal is disturbed by multipath effects in complex indoor environments, resulting in a serious decline of some RSSs. Unreliable signals increase the fingerprint mismatches.
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