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