A Self-Adaptive Model-Based Wi-Fi Indoor Localization Method

Abstract

This paper presents a novel method for indoor localization, developed with the main aim of making it useful for real-world deployments. Many indoor localization methods exist, yet they have several disadvantages in real-world deployments—some are static, which is not suitable for long-term usage; some require costly human recalibration procedures; and others require special hardware such as Wi-Fi anchors and transponders. Our method is self-calibrating and self-adaptive thus maintenance free and based on Wi-Fi only. We have employed two well-known propagation models—free space path loss and ITU models—which we have extended with additional parameters for better propagation simulation. Our self-calibrating procedure utilizes one propagation model to infer parameters of the space and the other to simulate the propagation of the signal without requiring any additional hardware beside Wi-Fi access points, which is suitable for real-world usage. Our method is also one of the few model-based Wi-Fi only self-adaptive approaches that do not require the mobile terminal to be in the access-point mode. The only input requirements of the method are Wi-Fi access point positions, and positions and properties of the walls. Our method has been evaluated in single- and multi-room environments, with measured mean error of 2–3 and 3–4 m, respectively, which is similar to existing methods. The evaluation has proven that usable localization accuracy can be achieved in real-world environments solely by the proposed Wi-Fi method that relies on simple hardware and software requirements.

Automatic Detection of Changes in Signal Strength Characteristics in a Wi-Fi Network for an Indoor Localisation System

“…Tuta et al [6] stressed the issue of developing an indoor positioning system with the main aim of making it useful for real-world deployments, including the creation of self-calibrating and self-adaptive systems. Several such localisation systems were proposed.…”

“…However, the adaptation is based on infrastructure of anchors of known location. Tuta et al [6] merged a free-space path loss model and a propagation model to create a self-calibrating and self-adaptive model. This procedure infers parametres of the space and simulates the propagation of the signal.…”

“…Although the detection of missing signals is critical for QoS, we extend this idea to detect all significant changes in the observed signals. Finally, to design a universal solution, we avoid signal propagation modelling [6] and the necessity of a knowledge of the Wi-Fi infrastructure scheme [7] using by fingerprinting and machine learning techniques.…”

Automatic Detection of Missing Access Points in Indoor Positioning System †

“…The proposed method reduces the drift error to less than one meter on a 35 m path. An alternative self-calibrating and self-adaptive model was proposed in [6], which merges two models, a free space path loss model and a propagation model. This self-calibrating procedure utilises one propagation model to infer parameters of the space and the other to simulate the propagation of the signal.…”

“…Another essential feature of our localisation model is that it provides the localisation based only on a single reading not taking into account the readings taken previously. Although it is not a dead reckoning solution, it can be a base model to create solutions by using previously determined positions in the localisation process (see for example [5,6]).…”