Smart-Phone Based Improved Multi-floor Indoor Localization System

Abstract

In recent years, smart-phone based multi-floor indoor localization has been received widespread attention due to skyscrapers buildings. The methods based on Wi-Fi fingerprinting approach are widely adopted to estimate the floor location and 2D geographical coordinates of the mobile user. However, they must deal with huge calibration effort required to build the labeled dataset. This issue was also addressed in the competition organized by an international conference on “Indoor Positioning and Indoor Navigation” (IPIN-2016). It is still a crucial task to develop an accurate and fast localization system using low calibration effort. This chapter utilizes the IPIN-2016 dataset and proposes an improved localization system that mainly works into three subparts as (i) building identification, (ii) floor identification, and (iii) 2D geographical coordinate’s estimation. For identifying the correct building, Wi-Fi majority rule based approach is applied and achieved 100% accuracy. This work also applies the fuzzy based clustering algorithm on atmospheric pressure data to identify the floor and achieves the accuracy of 98.68%. Further, the proposed localization system enhances the IPIN-2016 Wi-Fi fingerprinting dataset by exploiting the measurements of inertial sensors. Then, it uses a linear regression method to determine the 2D geographical coordinates and obtains better accuracy than the winning and runner-up team of IPIN-2016 with the mean localization error of 3.57 meters.