Indoor Positioning System has been one of the most attractive research after Bluetooth Low Energy (BLE) was introduced. This technology mainly used because of the reduction of material and energy cost over time that has huge impact compared to other technologies, which are more costly. Most recent research resolve around improving the accuracy of calculated position of the user by implementing different method to enable an indoor positioning system, and to remove any noises in the dataset. This paper objective is to compare some of the available methods that are used to enable Indoor Positioning System such as Fingerprinting, Multilateration, Trilateration, and Heron Bilateration. Since the performance of Fingerprinting is better compared to other methods, we combine Fingerprinting’s offline phase with the other methods to create a hybrid method and compare the accuracy of predicted user’s position. The experimental results show that the Fingerprinting and WKNN method outperform all other methods by resulting on 271.76 cm mean of error.
Performance Evaluation of Multilateration and Fingerprinting Method in Indoor Positioning System
