Abstract
Recent developments in smartphone technology have increased user demand for indoors applications. The Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) are the two advanced systems for navigation technology. However, it is still difficult for GNSS to provide an accurate and practical navigation solution, especially in environments with little or no signal availability. These failings should be easy to overcome; because of their portability and multiple embedded hardware sensors, smartphones seem well positioned to make pedestrian navigation easy and convenient in any environment. Pedestrian Dead Reckoning (PDR) is one of the most commonly used technologies used for pedestrian navigation, but it is not always accurate because its positioning errors tend to accumulate over time. Therefore, this research introduces a new tool to overcome this failing; a Bluetooth Low-Energy (BLE) beacon can maintain and improve the accuracy of PDR. Moreover, a BLE beacon can be initialized from any user position in an indoor environment. The random and unpredictable positions of pedestrians inevitably result in the degradation of navigation guidance systems’ data. To rectify this problem, we have implemented activity recognition technology to notify the proposed system so as to provide a more accurate heading estimate. This study proposes a Personal Navigation System (PNS) based on this technology; it can estimate navigation solutions in real time and combines the advantages of PDR and Bluetooth positioning technology. A series of experiments were conducted to evaluate the accuracy of the system and the efficacy of our proposed algorithms. Preliminary results show the average relative precision of PDR to be about 2.5%, when using a mobile hand-held device. The error of initial position from 2-D beacon positioning is less than two meters. The proposed system works well without post-processing, and the multi-sensor activity recognition system can determine the placement of the device when it is being carried or used by someone with close to 100% accuracy.
Highlights
Over the past twenty years, various kinds of indoor navigation systems have been developed based upon different theories and equipment [1]
The results show an accuracy of about 2 m in N and E directions
This research proposes a multi-sensor activity recognition system based on a pattern recognition
Summary
Over the past twenty years, various kinds of indoor navigation systems have been developed based upon different theories and equipment [1]. These days, mobile communications technology has caused mobile devices to become nearly ubiquitous because they are multi-functional, easy to carry and are no longer very expensive. They have changed our lives in substantial ways. Satellite System (GNSS) chips, accelerometers, gyroscopes, magnetometers, barometers, Bluetooth chips, and Wi-Fi chips. This makes the smartphone a potentially ideal mobile navigator.
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