Signal Processing - September 2017 - 125

Dimitrios Lymberopoulos and Jie Liu

The Microsoft
Indoor
Localization
Competition
©ISTOCKPHOTO.COM/ANDRESR

Experiences and lessons learned

W

e present the results, experiences, and lessons learned
from comparing a diverse set of indoor location technologies during the Microsoft Indoor Localization
Competition. Over the last four years (2014-2017),
more than 100 teams from academia and industry deployed
their indoor location solutions in realistic, unfamiliar environments, allowing us to directly compare their accuracies
and overhead. In this article, we provide an analysis of this
four-year-long evaluation study's results and discuss the current
state of the art in indoor localization.

Overview of the indoor location problem
Accurate indoor localization has the potential to transform the
way people navigate indoors in the same way the global positioning system (GPS) changed the way people navigate outdoors. For well over a decade, academia and industry have
recognized the value of the indoor localization problem and
have devoted much effort and resources into solving it.
Infrastructure-free approaches have focused on leveraging
already existing Wi-Fi [1]-[10], frequency modulation (FM)
and TV [11]-[18], global system for mobile communication
Digital Object Identifier 10.1109/MSP.2017.2713817
Date of publication: 6 September 2017

1053-5888/17©2017IEEE

[19], [20], geomagnetic [21], and sound signals [22] to enable
indoor localization through detailed fingerprinting. In frastructure-based approaches rely on the deployment of customized radio-frequency (RF) beacons [23], such as RF
identification [24], infrared [25], ultrasound [26], [27],
Bluetooth [28], short-range FM transmitters (Tx) [29], lights
[30], and magnetic signal modulators [31], [32] to enable accurate indoor position estimation.
Even though hundreds of different approaches have been
proposed in the literature, the indoor location problem still
remains unsolved. The research community has not converged to a single, widely accepted solution that can achieve
the desired accuracy at the required cost. We believe that
this is partly due to the highly ad hoc evaluation process of
indoor location systems. Each system is usually evaluated
in a custom, highly controlled environment making it hard
to draw conclusions about its performance and overhead
in realistic conditions. Even worse, this type of evaluation
makes comparisons of different indoor location solutions
almost impossible.
With this in mind, we organized the Microsoft Indoor
Localization Competition [33]-[37]. The main motivation
behind the competition was to give the opportunity to different academic and industry groups to test their indoor

IEEE SIGNAL PROCESSING MAGAZINE

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September 2017

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Table of Contents for the Digital Edition of Signal Processing - September 2017

Signal Processing - September 2017 - Cover1
Signal Processing - September 2017 - Cover2
Signal Processing - September 2017 - 1
Signal Processing - September 2017 - 2
Signal Processing - September 2017 - 3
Signal Processing - September 2017 - 4
Signal Processing - September 2017 - 5
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Signal Processing - September 2017 - 7
Signal Processing - September 2017 - 8
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Signal Processing - September 2017 - 125
Signal Processing - September 2017 - 126
Signal Processing - September 2017 - 127
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Signal Processing - September 2017 - Cover3
Signal Processing - September 2017 - Cover4
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