Abstract
With the rapid development of smartphones and wireless networks, indoor location-based services have become more and more prevalent. Due to the sophisticated propagation of radio signals, the Received Signal Strength Indicator (RSSI) shows a significant variation during pedestrian walking, which introduces critical errors in deterministic indoor positioning. To solve this problem, we present a novel method to improve the indoor pedestrian positioning accuracy by embedding a fuzzy pattern recognition algorithm into a Hidden Markov Model. The fuzzy pattern recognition algorithm follows the rule that the RSSI fading has a positive correlation to the distance between the measuring point and the AP location even during a dynamic positioning measurement. Through this algorithm, we use the RSSI variation trend to replace the specific RSSI value to achieve a fuzzy positioning. The transition probability of the Hidden Markov Model is trained by the fuzzy pattern recognition algorithm with pedestrian trajectories. Using the Viterbi algorithm with the trained model, we can obtain a set of hidden location states. In our experiments, we demonstrate that, compared with the deterministic pattern matching algorithm, our method can greatly improve the positioning accuracy and shows robust environmental adaptability.
Highlights
Nowadays, with the rapid development of smartphones and wireless networks, people can conveniently access location-based services (LBSs) with mobile applications
We have taken a series of measures to improve the Received Signal Strength Indicator (RSSI) measurement accuracy
Cumulative distribution function of error (CDF) and root mean square error (RSME) to calculate theIf not otherwise specified, positioning error. all measurements are collected with this smartphone
Summary
With the rapid development of smartphones and wireless networks, people can conveniently access location-based services (LBSs) with mobile applications. The outdoors localization technology is relatively mature, and mainly includes the Global Positioning System (GPS) and cellular positioning systems [1]. For indoor localization, GPS signals cannot reach the receivers and the positioning accuracy is too low to support indoor services. For this purpose, people have done a lot of research with different technologies, and developed RFID positioning systems, infrared ray positioning systems, Bluetooth positioning systems, ZigBee positioning systems, Sensors 2016, 16, 1447; doi:10.3390/s16091447 www.mdpi.com/journal/sensors positioning systems, infrared ray positioning systems, Bluetooth positioning systems, ZigBee positioning systems, ultrasonic positioning systems, vision positioning systems, voice recognition ultrasonic positioning systems, vision positioning systems, voice recognition positioning systems, positioning systems, and WLAN (Wireless Local Area Network) fingerprint positioning systems, and WLAN
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