Received signal strength–based indoor localization using a robust interacting multiple model–extended Kalman filter algorithm

Ana Moragrega,Jordi Vilà-Valls,Juan Manuel Castro-Arvizu,Juan A Fernandez-Rubio,Pau Closas

doi:10.1177/1550147717722158

Abstract

Due to the vast increase in location-based services, currently there exists an actual need of robust and reliable indoor localization solutions. Received signal strength localization is widely used due to its simplicity and availability in most mobile devices. The received signal strength channel model is defined by the propagation losses and the shadow fading. In real-life applications, these parameters might vary over time because of changes in the environment. Thus, to obtain a reliable localization solution, they have to be sequentially estimated. In this article, the problem of tracking a mobile node by received signal strength measurements is addressed, simultaneously estimating the model parameters. Particularly, a two-slope path loss model is assumed for the received signal strength observations, which provides a more realistic representation of the propagation channel. The proposed methodology considers a parallel interacting multiple model–based architecture for distance estimation, which is coupled with the on-line estimation of the model parameters and the final position determination via Kalman filtering. Numerical simulation results in realistic scenarios are provided to support the theoretical discussion and to show the enhanced performance of the new robust indoor localization approach. Additionally, experimental results using real data are reported to validate the technique.

Highlights

The need for localization is not just confined to people or vehicles in outdoor environments, where Global Navigation Satellite System (GNSS) plays an important role for this purpose and is recognized to be the legacy solution
In section ‘‘Simulation results,’’ the results of the IMMEKF algorithm were obtained with synthetic signal and in section ‘‘Validation with real data’’ with real data
The method proposed in this work was validated by computer simulations in a scenario depicted in Figure 5 that could be considered as a realistic scenario and where the number of access points (APs) (N = 6) were deployed in a 30 3 30 m2 area at known locations

Summary

Introduction

The need for localization is not just confined to people or vehicles in outdoor environments, where Global Navigation Satellite System (GNSS) plays an important role for this purpose and is recognized to be the legacy solution. A way of localization in indoor environments is using available radio signals such as wireless local area network (WLAN) (IEEE 802.11x), Zigbee, and ultra-wideband. The advantage of working with signals of the IEEE 802.11 as the primary source of information to approach the localization problem is the inexpensive hardware and the already dense deployment of WLAN access points (APs) in urban areas. The IEEE 802.11x model is considered because it does not require an accurate floor plan of the indoor scenario and can be implemented without using a third-party software. There are several channel models in the literature to characterize the indoor propagation environment. In this article, the IEEE 802.11x model is considered because it does not require an accurate floor plan of the indoor scenario and can be implemented without using a third-party software.

Methods

Results

Conclusion