Improving Wi-Fi based Indoor Positioning using Particle Filter based on Signal Strength

Abstract

Indoor positioning is recognized as one of the upcoming major applications which can be used in wide variety of applications such as indoor navigation and enterprise asset tracking. The significance of localization in indoor environments have made the use of Wi-Fi based indoor positioning so that it can utilize available current wireless infrastructure and perform positioning very easily. In this paper we introduced a user friendly prototype for Wi-Fi based indoor positioning system where a user can identify its own position in indoor. Wi-Fi received signal strength (RSS) fluctuations over time introduce incorrect positioning To minimize the fluctuation of RSS, we developed Particle Filters with the prototype. A comparison between with and without Particle Filter for error performance is presented and at the same time it is also noticed that variation in number of particles could change the positioning accuracy. Moreover comparison between calibration data in all directions and in one direction while constructing a radio map is presented