A Bayesian Approach to Dealing with Device Heterogeneity in an Indoor Positioning System

There are many practical applications in which the ability to localize devices such as phones, tablets, and mobile equipment is important. One of the issues which makes this difficult is the fact that devices are all different, so an approach which is robust against device heterogeneity would be an advance. In this paper, a method for estimating the positions of transmitting devices using Wi-Fi and a network of access points (APs) is proposed and investigated. The APs can also function as transmitters; as such the method allows simultaneous calibration and localization, so no fingerprinting or separate calibration is required. A hierarchical Bayesian probabilistic model is used with separate but conditionally-related parameters for each transmitter and receiver to tackle the device inhomogeneity problem. The output is a probability distribution over the location of each device from which the expected location and measures of uncertainty in location can be obtained. The system was implemented in an office environment using heterogeneous transmitters and receivers. The system localized the devices with a median error of 1.7 meters and within 4.32 meters with 95% confidence. We discovered that it is more important to account for inhomogeneity in the transmitters than in the receivers. Removing the former from the model results in a median error of 6.57 m(10.56 m) whereas removing the latter results in a median error 1.93 m(4.64 m), We argue that the technique could be used to cope with other types of inhomogeneities in the environments or the Wi-Fi equipment.