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

Alireza Razavi¹, Mikko Valkama², Elena Simona Lohan³

¹Department of Electronics and Communications Engineering, Tampere University of Technology, Tampere 33720, Finland. arazavi@ieee.org.

Sensors (Basel, Switzerland)

|June 4, 2016

Summary

This study enhances floor detection for mobile devices using robust probabilistic methods. By improving received signal strength (RSS) algorithms, it increases accuracy for indoor 3D localization.

Keywords:

RSS-based localization floor detection indoor localization robust regression trilateration weighted centroid localization

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Area of Science:

Wireless communication
Mobile computing
Indoor localization

Background:

Accurate floor detection is crucial for 3D indoor localization of mobile devices.
Existing approaches often lack robustness and are building-dependent.
Robustness is key for reliable wireless positioning.

Purpose of the Study:

To robustify floor estimation in probabilistic indoor localization methods.
To develop building-independent and computationally efficient localization algorithms.
To enhance the performance of Received Signal Strength (RSS)-based floor detection.

Main Methods:

Employing probabilistic approaches with a low number of parameters for floor estimation.
Developing four robustified algorithms: weighted centroid, linear trilateration, nonlinear trilateration, and deconvolution.
Utilizing Received Signal Strengths (RSS) from WiFi access points (APs) for vertical position estimation.

Main Results:

Demonstrated that robustification significantly increases the performance of RSS-based floor detection.
Achieved building-independent estimation with lower computational power requirements.
Validated the effectiveness of the proposed robustified algorithms.

Conclusions:

Robustification is an effective strategy to improve RSS-based floor detection algorithms.
The proposed methods offer a more reliable and efficient solution for indoor 3D localization.
This work contributes to advancing mobile device positioning in wireless environments.