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Time-based Indoor Positioning and Context Information using Commodity WiFi chipsets

Rea, Maurizio (2020) Time-based Indoor Positioning and Context Information using Commodity WiFi chipsets. PhD thesis, Universidad Carlos III de Madrid, Spain.

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In the last years indoor localization and applications that use positioning information have attracted a lot of attention from the research community as well as the industry. The achieved accuracy of an indoor localization system is the key to enable certain applications, such as navigation. In a scenario where the system runs exclusively on commodity hardware such as smartphones and even without installing any mobile app in the mobile device, location information may be exploited not only for navigation, but also for the benefit of the network itself or for the investigation of physical behaviours. In indoor areas, communication technologies such as Wireless Fidelity (WiFi) are gaining popularity due to the ever increasing availability and deployment of APs that can be used simultaneously as an infrastructure for networking and positioning. Despite being the most used technology for tracking devices in indoor environments, the research community has focused more intensively on approaches such as the Received Signal Strength Indicator (RSSI) or combining WiFi signals with inertial sensors as found in smartphones. These approaches are error prone, dependent on frequent calibrations, or they require specialized hardware or the user must be interested to run a specific application designed to run on the smartphone. Using only commodity WiFi chipsets, this thesis investigates location systems that are based on Time-of-Flight (ToF) echo technique, and do not require any calibration and user intervention, and it explores ranging and location data to improve network management and infer user behaviour. ToF technique has been modestly exploited for indoor localization, whereas ToF is successfully used in Global Positioning System (GPS) in outdoor environments. The reason is that WiFi ToF measurements, mainly extracted from commodity chipsets, suffer from extensive device-related noise which makes it challenging to differentiate between direct path from non-direct path signal components when estimating the ranges. Existing multipath mitigation techniques tend to fail at identifying the direct path when the devicerelated Gaussian noise is in the same order of magnitude, or larger than the multipath noise. In order to address this challenge, we first propose in this thesis a new method for filtering ranging measurements, extracted from a commodity WiFi chipset, that is better suited for the inherent large noise as found in WiFi radios. Our proposed filter combines statistical learning and robust statistics and it does not require specialized hardware, the intervention of the user, or cumbersome on-site manual calibration. This makes the method we propose as the first contribution of the present work particularly suitable for indoor localization in large-scale deployments using existing legacy WiFi infrastructures. We build and investigate a multi WiFi ToF-based APs localization system, where these filtered timing signals are used as ranging inputs of the multi-lateration problem for positioning. We then deploy and evaluate our system for indoor mobile tracking scenarios in many multipath-rich environments, across multiple testbeds which cover different surfaces and further test it in Microsoft indoor localization competitions, demonstrating that, despite these challenges, our technique can achieve distance accuracy comparable to other approaches proposed by the start of the art but does not share their aforementioned shortcomings. The deployment of a multi-APs positioning system is targeted to specific areas, such as large offices and shopping malls. In order to bring indoor positioning also to homes and small businesses which typically have a single AP, the next step we envision towards a hybrid single-AP positioning system is a deep inspection and interaction of the Fine Time Measurements (FTM), a type of ToF echo technique standardized recently, and Channel State Information (CSI) for ranging and Angle-Of-Arrival (AOA) for angle estimates, respectively. We exploit Physical Layer (PHY) information to detect the number of paths and their directions and we use this information to derive a new method for filtering ranging measurements obtained with the FTM protocol. We achieve sub-meter distance estimation accuracy eliminating the adverse effect of multipath in FTM using calibrated inputs from CSI. We then evaluate the system in multipath-rich environments, demonstrating the capability of the combination of AOA estimation and the proposed FTM refinement approach to achieve reasonable positioning accuracy in areas comparable to typical flat sizes just using a commodity smartphone as target device. All the contributions described so far and all the extracted location data may be exploited also in the network core to better allocate network resources based on the expected link performance. Thus, we take advantage of positioning data and more in general context information, to enable reliable mobile communications via advanced resource management policies and adaptive traffic engineering strategies. Of particular interest for this thesis is to investigate usage of positioning data in industrial environments, where the presence of metallic objects challenge the reliability of wireless communication. With the advent of the fourth Industrial revolution (Industry 4.0) such harsh environments have attracted high attention and, for this reason, of particular interest for this thesis is also the deployment of our multi-APs system in such industrial environment. The latter, due to blockage and strong reflections, is notorious for being adverse to wireless communication, impacting on the signal quality. We propose to exploit the knowledge of location to derive context information to dynamically allocate wireless resources in time and space to target devices. We exploit the spatial geometry of the APs and a statisticalmodel that maps the user position’s spatial distribution to an angle error distribution to derive a hypothesis test to declare if the link is in Line-Of-Sight (LOS) or Non-Line- Of-Sight (NLOS). In order to avoid changes to the client side and operate with a single interface radio, we use the same wireless network both for positioning and scheduling. We experimentally show that context information applied to wireless resources protocol help increasing the network throughput in the aforementioned industrial-like scenario. Finally, taking advantage of ranging information extracted from FTM, we propose to estimate the device movement without any access to physical inertial sensors in the mobile. The idea is to infer the movements of the mobile through radio measurements, a concept we call ”virtual inertial sensors”. We propose a method for estimating the user walking speed and a novel method for the rotation of a mobile device. We evaluate and demonstrate the proposed approaches with experiments, and we compare the rotation method with a possible solution that explores CSI measurements. While FTM works with only one single antenna, it achieves better performance than a CSI-based estimator that exploits four antennas and multiple sub-carriers at the AP, but are yet limited by the typical one single WiFi antenna at the smartphone side. This new concept of virtual inertial sensors can be leveraged by location systems and sensing mechanisms to improve localization accuracy, infer user behavior, and design better and more secure communication. In conclusion, in this thesis we experimentally demonstrate that ToF data, extracted from commodity WiFi chipsets, can be used for tracking devices and provide context information in indoor environments without the need for environmental calibration and installations of mobile apps, but only with standard-compliant measurements performed from the WiFi infrastructure.

Item Type: Theses (PhD)
Depositing User: Marta Dorado
Date Deposited: 18 Jun 2020 07:29
Last Modified: 18 Jun 2020 07:29

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