A statistical analysis on the system performance of a Bluetooth Low Energy indoor positioning system in a 3D environment

Abstract

Since GPS tends to fail for indoor positioning purposes, alternative methods like indoor positioning systems (IPS) based on Bluetooth low energy (BLE) are developing rapidly. Generally, IPS are deployed in environments covered with obstacles such as furniture, (partition-) walls, people and electronics influencing the signal propagation. The major factor influencing the system performance and to acquire optimal positioning results is the geometry of the beacons. The geometry of the beacons is limited to the available infrastructure that can be deployed (number of beacons, basestations and tags), which is dependent on the budget and deployment effort of the customer.
This leads to the following challenge: Given a limited number of beacons, where should they be placed in a specified indoor environment, such that the geometry contributes to optimal positioning results?

This challenge is approached by using theoretical design computations. The design computations require the definition of a chosen 3D space, the number of beacons, possible user tag locations and a performance threshold (e.g. required precision). For any given set of beacon and receiver locations, the precision, internal- and external reliability can be determined on forehand. The results of a given geometry have been validated by deploying an IPS of BlooLoc and comparing the observed precision with the modeled precision for a chosen set of user tag locations. The theoretical model showed a precision pattern with several equivalent precision patterns of the measured data, however, some significant differences could not be explained physically and the model has to be adapted further. Besides determining the precision based on a set of beacon and receiver locations, the model is able to select the optimal geometric configuration based on a performance threshold (e.g. required precision). Depending on the performance threshold, the optimal configurations can either be a single solution or consist of multiple solutions that satisfy the performance threshold of the customer. The performance threshold varies depending on the use case and the user requirements. Therefore, the amount of possible combinations in terms of 3D space, amount of available beacons, possible beacon locations, user tag locations and performance thresholds are limitless and the model can thus be used for all kind of applications.

All in all, the initial model (design computations) can be used by IPS customers for all kind of applications. The model is able to select the optimal geometric configuration in terms of precision based on a performance threshold specified by the user. Furthermore, the model requires user specified input parameters and the amount of possible combinations are therefore unlimited. Although the initial model has to be adapted further to account for the differences in modeled and measured data as a consequence of environmental factors, the initial model is a good initiative for the rising indoor positioning market and its customers. Therefore, the model can be adapted further such that it can explain significant differences between the modeled and the measured data by including factors that influence the system performance in real life, such as materialistic properties, signal attenuation, interference, multipath, NLOS etc.