Robust Statistical Approaches for RSS-Based Floor Detection in Indoor Localization.

Alireza Razavi,Mikko Valkama,Elena Lohan

doi:10.3390/s16060793

Abstract

Floor detection for indoor 3D localization of mobile devices is currently an important challenge in the wireless world. Many approaches currently exist, but usually the robustness of such approaches is not addressed or investigated. The goal of this paper is to show how to robustify the floor estimation when probabilistic approaches with a low number of parameters are employed. Indeed, such an approach would allow a building-independent estimation and a lower computing power at the mobile side. Four robustified algorithms are to be presented: a robust weighted centroid localization method, a robust linear trilateration method, a robust nonlinear trilateration method, and a robust deconvolution method. The proposed approaches use the received signal strengths (RSS) measured by the Mobile Station (MS) from various heard WiFi access points (APs) and provide an estimate of the vertical position of the MS, which can be used for floor detection. We will show that robustification can indeed increase the performance of the RSS-based floor detection algorithms.

Highlights

Indoor localization is becoming more and more important in today’s wireless world
Assuming that we hear an average of 30 access points (APs) in each location point inside a building, that we take measurements from an average of 600 location points per building, that there are 25 important buildings in the location area where the mobile was identified by the network, a total of 495, 000 parameters would need to be stored in the database pertaining to that town and transferred to the mobile
Example 1—Robust weighted centroid localization with real data: The first example studies the performance of ordinary Weighted Centroid Localization (WCL) and robust WCL approaches with four different robust methods: Huber with parameter k = 1.345 (Hub1), Huber with parameter k = 0.9 (Hub2), Bi-square with parameter k = 4.685 (Bsq), and Cauchy with parameter k = 2.385 (Cau)

Summary

Introduction

Indoor localization is becoming more and more important in today’s wireless world. Being able to achieve accurate ubiquitous localization on hand-held battery operating mobile devices in both indoor and outdoor environments would open the window to many new Location Based Services (LBS). If a global localization solution would use a fingerprinting approach, the fingerprint database transferred from the server to the MS would include the fingerprints from all essential buildings in the town (or the location area) where the mobile is situated. Assuming that we hear an average of 30 APs in each location point inside a building, that we take measurements from an average of 600 location points per building, that there are 25 important buildings (malls, shopping centers, hospitals, airports, etc.) in the location area where the mobile was identified by the network, a total of 495, 000 parameters would need to be stored in the database pertaining to that town and transferred to the mobile.

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Conclusion