Seamless Navigation using UWB-based Multisensor System

This work presents an Ultra-wideband-based (UWB) approach to seamless positioning and navigation applied in a real test-bed. It deploys two different solutions for positioning estimation in function of the operational environment. Outdoors, a classical hybridization between Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) is applied while indoors, an UWB/INS integration is performed relying on a low-cost commercial platform which integrates both UWB unit and IMU. The implementation of this procedure will be presented with more details in the paper. The aim of the work is to validate the performances in term of accuracy, precision and seamlessness behavior of the low-cost UWB technology available today. The results shown an overall accuracy of about 60 cm considering the entire path walked, both outdoor and indoors

Crowd sourced self beacon mapping with isolated signal aware bluetooth low energy positioning

In the past few decades, there has been an increase in the demand for positioning and navigation systems in various fields. Location-based service (LBS) usage covers a range of different variations from advertising and navigation to social media. Positioning based on a global navigation satellite system (GNSS) is the commonly used technology for positioning nowadays. However, the GNSS has a limitation of needing the satellites to be in line-of-sight (LOS) to provide an accurate position. Given this limitation, several different approaches are employed for indoor positioning needs. Bluetooth low energy (BLE) is one of the wireless technologies used for indoor positioning. However, BLE is well-known for having unstable signals, which will affect an estimated distance. Moreover, unlike Wi-Fi, BLE is not commonly and widely used, and BLE beacons must thus be placed to enable a venue with BLE positioning. The need to deploy the beacons results in a lengthy process to place and record the position of each placed beacon. This thesis proposes several solutions to solve these problems. A filter based on a Fourier transform is proposed to stabilise a BLE signal to obtain a more reliable reading. This allows the BLE signals to be less affected by internal variation than unfiltered signal. An obstruction-aware algorithm is also proposed using a statistical approach, which allows for the detection of non-line-of-sight (NLOS). These proposed solutions allow for a more stable BLE signal, which will result in a more reliable estimation of distance using the signal. The proposed solutions will enable accurate distance estimation, which will translate into improved positioning accuracy. An improvement in 88% of the test points is demonstrated by implementing the proposed solutions. Furthermore, to reduce the calibration needed when deploying the BLE beacons, a beacon-mapping algorithm is proposed that can be used to determine the position of BLE beacons. The proposed algorithm is based on trilateration with added information about direction. It uses the received signal strength (RSS) and the estimated distance to determine the error range, and a direction line is drawn based on the estimated error range. Finally, to further reduce the calibration needed, a crowdsource approach is proposed. This approach is proposed alongside a complete system to map the location of unknown beacons. The proposed system uses three phases to determine the user location, determine the beacons’ position, and recalculate BLE scans that have insufficient number of known BLE beacons. Each beacon and user’s position determined is assigned a weight to represent the reliability of that position. This is important to ensure that the position generated from a more reliable source will be emphasised. The proposed system demonstrates that the beacon-mapping system can map beacons with a root mean squared error (RMSE) of 4.64 m and a mean of absolute error (MAE) of 4.28 m