Dynamic spatial segmentation strategy based magnetic field indoor positioning system

In this day and age, it is imperative for anyone who relies on a mobile device to track and navigate themselves using the Global Positioning System (GPS). Such satellite-based positioning works as intended when in the outdoors, or when the device is able to have unobstructed communication with GPS satellites. Nevertheless, at the same time, GPS signal fades away in indoor environments due to the effects of multi-path components and obstructed line-of-sight to the satellite. Therefore, numerous indoor localisation applications have emerged in the market, geared towards finding a practical solution to satisfy the need for accuracy and efficiency. The case of Indoor Positioning System (IPS) is promoted by recent smart devices, which have evolved into a multimedia device with various sensors and optimised connectivity. By sensing the device’s surroundings and inferring its context, current IPS technology has proven its ability to provide stable and reliable indoor localisation information. However, such a system is usually dependent on a high-density of infrastructure that requires expensive installations (e.g. Wi-Fi-based IPS). To make a trade-off between accuracy and cost, considerable attention from many researchers has been paid to the range of infrastructure-free technologies, particularly exploiting the earth’s magnetic field (EMF). EMF is a promising signal type that features ubiquitous availability, location specificity and long-term stability. When considering the practicality of this typical signal in IPS, such a system only consists of mobile device and the EMF signal. To fully comprehend the conventional EMF-based IPS reported in the literature, a preliminary experimental study on indoor EMF characteristics was carried out at the beginning of this research. The results revealed that the positioning performance decreased when the presence of magnetic disturbance sources was lowered to a minimum. In response to this finding, a new concept of spatial segmentation is devised in this research based on magnetic anomaly (MA). Therefore, this study focuses on developing innovative techniques based on spatial segmentation strategy and machine learning algorithms for effective indoor localisation using EMF. In this thesis, four closely correlated components in the proposed system are included: (i) Kriging interpolation-based fingerprinting map; (ii) magnetic intensity-based spatial segmentation; (iii) weighted Naïve Bayes classification (WNBC); (iv) fused features-based k-Nearest-Neighbours (kNN) algorithm. Kriging interpolation-based fingerprinting map reconstructs the original observed EMF positioning database in the calibration phase by interpolating predicted points. The magnetic intensity-based spatial segmentation component then investigates the variation tendency of ambient EMF signals in the new database to analyse the distribution of magnetic disturbance sources, and accordingly, segmenting the test site. Then, WNBC blends the exclusive characteristics of indoor EMF into original Naïve Bayes Classification (NBC) to enable a more accurate and efficient segmentation approach. It is well known that the best IPS implementation often exerts the use of multiple positing sources in order to maximise accuracy. The fused features-based kNN component used in the positioning phase finally learns the various parameters collected in the calibration phase, continuously improving the positioning accuracy of the system. The proposed system was evaluated on multiple indoor sites with diverse layouts. The results show that it outperforms state-of-the-art approaches and demonstrate an average accuracy between 1-2 meters achieved in typical sites by the best methods proposed in this thesis across most of the experimental environments. It can be believed that such an accurate approach will enable the future of infrastructure–free IPS technologies