RT Dissertation/Thesis
T1 Algorithms for robust indoor localization and sensing using off-the-shelf devices using off-the-shelf devices
A1 Blanco Pizarro, Alejandro
A2 IMDEA Networks Institute, 
AB Localization has been mainly an optional feature of cellular networks since they havebeen designed for communication and many location-based services have used GPS toprovide more functionalities like navigation, rescue and many more. GPS gets outstandingaccuracy in outdoor environments, but its performance drastically degrades in indoorsettings since the GPS signals hardly go through walls. However, most human activitiesare concentrated in indoor environments and location-based services cannot be carried outsuccessfully by GPS. To overcome this limitation, wireless protocols are appealing to fulfillindoor localization requirements while communication is ongoing. Operators, chipsetvendors and application developers are paying attention to exploiting location informationto provide new applications like augmented reality and indoor navigation. Moreover,localization can be used for network optimization and researchers are actively investigatingit. For instance, intelligent handover can exploit location information to guess whichAccess Point (AP) is the most suitable one before doing the handover. In addition,a range of applications can exploit it as well such as Multiple-Input Multiple-Outputbeamforming, millimeter-wave beam alignment, etc. For the last decade, sensing hasbeen appealing to not only provide location information but also context awareness. Thisenables human activity and event recognition, vital sign monitoring, user identification,mapping, imaging, etc. To ensure the good performance of these applications, accurateand ubiquitous positioning is needed. To this end, 5G and the newest Wi-Fi protocols,IEEE 802.11ac and 802.11ax, are becoming the key technologies to provide outstandingindoor localization since they incorporate larger array configurations and wider channelbandwidths than previous wireless protocols.Researchers have made a great effort to provide indoor localization and decimeterlevel of accuracy has been achieved. However, this outstanding performance has beenevaluated using a great number of APs and assuming that every AP has a clear Line-Of-Sight (LOS) to the device. However, typical indoor wireless deployments tend to havesparse AP densities since they are optimized for coverage and not for localization. Forinstance, a Wi-Fi infrastructure usually contains one AP per room and a 5G deploymenttends to have a limited number of AP as well. Moreover, indoor environments are generallyrich in multipath components that interfere with the estimation of the direct path. This is particularly challenging in Non-Line-Of-Sight (NLOS) settings as obstacles can blockthe direct path and a system might detect an NLOS path and not the obstructed LOSpath. As a result, the performances of state-of-the-art localization schemes drasticallydegrade their accuracy in realistic deployments.A localization algorithm that copes well with NLOS settings and wireless deploymentswith sparse AP densities is needed for precise and pervasive localization. Also,implementing and testing it in cutting edge devices is crucial to exploit the improvedhardware features of the newest wireless protocols. Therefore, this thesis aims at providinga framework for accurate localization even in challenging scenarios. Sensing researchshares methodologies with localization since sensing applications require extractinglocation information from NLOS paths as localization does from the direct path. Hence,this thesis also aims at exploring how the proposed localization framework can be usedfor sensing applications.We start delving into wireless localization by exploring what an LTE localizationsystem can achieve. This is particularly beneficial since 5G and LTE will coexist for a whileuntil 5G provides ubiquitous coverage. Therefore, LTE needs to fulfill the localizationrequirements for a range of applications if 5G is not available. To this end, we implementand evaluate an LTE localization system for a single AP using software-defined radios.We observe that LTE achieves a median error of 2 m in LOS cases. However, the LTEperformance drastically degrades to 4.6 m of median error in NLOS settings. These resultspoint out that LTE provides a positioning accuracy that complies with a great numberof location-based services in LOS. Nevertheless, applications that demand ubiquitouslocalization may not be correctly carried out in NLOS settings.To tackle the NLOS issue, we implement UbiLocate, a Wi-Fi location systemthat copes well with common AP deployment densities and works ubiquitously, i.e.,without excessive degradation under NLOS. UbiLocate demonstrates that meter-levelmedian accuracy NLOS localization is possible through (i) an innovative angle estimatorbased on a Nelder-Mead search, (ii) a fine-grained time of flight ranging system withnanosecond resolution, and (iii) the accuracy improvements brought about by the increasein bandwidth and number of antennas of IEEE 802.11ac. In combination, they providesuperior resolvability of multipath components, significantly improving location accuracyover prior work. We implement our location system on off-the-shelf 802.11ac devices. Ourexperimental evaluation shows an overall improvement of the localization performance bya factor of 2-3.The latest generation of Wi-Fi standards, IEEE 802.11ax, brings new hardwarecapabilities that improve the performance of localization and sensing systems. Inparticular, the 160MHz of channel bandwidth and the four times denser spectrumsignificantly improve the resolvability of the multipath components compared to itspredecessor, IEEE 802.11ac. We present the first tool to collect the most accurate CSI ever from off-the-shelf devices. To further validate the platform, we carry outa preliminary measurement campaign to compare the localization accuracy of IEEE802.11ax with 802.11ac. Our results show that, as expected, IEEE 802.11ax providessuperior performance improving the accuracy by a factor of 1.75 for LOS and NLOSsettings.Sensing research goes beyond localization since it aims at providing context awareness.We explore the integration of the proposed multipath decomposition algorithm as wellas the testbed for sensing applications. In particular, we tackle human respiration rateestimation since it is appealing as it does not require any specialized hardware. Ourresults show that an accurate respiration rate estimation is possible by decomposing thechannel.In summary, location-based services demand accurate and ubiquitous localization.However, the state-of-the-art localization systems do not cope well with realisticwireless deployments and their positioning performances drastically degrade in theseenvironments. Hence, we provide a localization framework that copes well with realisticwireless deployments and with NLOS settings. We conclude that resolving accurately themultipath components enables pervasive and precise localization. In addition, sensingenables new applications that are helpful in many issues since it provides not only locationbut also context awareness. Hence, we show that algorithms and testbeds that aredesigned for localization can be also utilized for sensing applications by tackling respirationrate estimation.
YR 2022
FD 2022-03
LK https://hdl.handle.net/10016/35859
UL https://hdl.handle.net/10016/35859
LA eng
NO Mención Internacional en el título de doctor
NO This work has been supported by IMDEA Networks Institute
DS e-Archivo
RD 16 jul. 2024