Indoor Localization Using Trilateration and Location Fingerprinting Methods

Abstract

In this chapter, two algorithms for the determination of the indoor location of people are discussed. The trilateration algorithm uses the mathematical techniques to identify the location of the intersection point of three circles. A location “fingerprinting” algorithm is normally comprised of two stages. In the first stage, a positioning fingerprint database is established and the second stage is matching the fingerprint with the database. Kalman filter-based filtering technique is used to reduce the noise in the raw received signal strength indicator data. In order to evaluate these algorithms, a low-cost Bluetooth Low-Energy indoor localization system is proposed, and experiments have been carried out in different settings including a line-of-sight scenario and a non-line-of-sight scenario. Real-world testing has been done in three different apartment accommodations. The results show that in both the line-of-sight and non-line-of-sight experiments, the error is less than 0.5 meter within 3 meters in distance prediction by path loss models. The experimental results show that the trilateration localization algorithm is prone to error. The location fingerprinting-based method shows good accuracy in both grid-based scenario and location of interest-based scenario. The accuracy for both grid-based and location of interest-based scenarios is above 90%.