Algorithms for robust indoor localization and sensing using off-the-shelf devices

Abstract

Localization has been mainly an optional feature of cellular networks since they have been designed for communication and many location-based services have used GPS to provide more functionalities like navigation, rescue and many more. GPS gets outstanding accuracy in outdoor environments, but its performance drastically degrades in indoor settings since the GPS signals hardly go through walls. However, most human activities are concentrated in indoor environments and location-based services cannot be carried out successfully by GPS. To overcome this limitation, wireless protocols are appealing to fulfill indoor localization requirements while communication is ongoing. Operators, chipset vendors and application developers are paying attention to exploiting location information to provide new applications like augmented reality and indoor navigation. Moreover, localization can be used for network optimization and researchers are actively investigating it. For instance, intelligent handover can exploit location information to guess which Access 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-Output beamforming, millimeter-wave beam alignment, etc. For the last decade, sensing has been appealing to not only provide location information but also context awareness. This enables human activity and event recognition, vital sign monitoring, user identification, mapping, imaging, etc. To ensure the good performance of these applications, accurate and 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 outstanding indoor localization since they incorporate larger array configurations and wider channel bandwidths than previous wireless protocols.

Researchers have made a great effort to provide indoor localization and decimeter level of accuracy has been achieved. However, this outstanding performance has been evaluated 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 have sparse AP densities since they are optimized for coverage and not for localization. For instance, a Wi-Fi infrastructure usually contains one AP per room and a 5G deployment tends to have a limited number of AP as well. Moreover, indoor environments are generally rich 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 block the direct path and a system might detect an NLOS path and not the obstructed LOS path. As a result, the performances of state-of-the-art localization schemes drastically degrade their accuracy in realistic deployments.

A localization algorithm that copes well with NLOS settings and wireless deployments with sparse AP densities is needed for precise and pervasive localization. Also, implementing and testing it in cutting edge devices is crucial to exploit the improved hardware features of the newest wireless protocols. Therefore, this thesis aims at providing a framework for accurate localization even in challenging scenarios. Sensing research shares methodologies with localization since sensing applications require extracting location 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 used for sensing applications.

We start delving into wireless localization by exploring what an LTE localization system can achieve. This is particularly beneficial since 5G and LTE will coexist for a while until 5G provides ubiquitous coverage. Therefore, LTE needs to fulfill the localization requirements for a range of applications if 5G is not available. To this end, we implement and 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 LTE performance drastically degrades to 4.6~m of median error in NLOS settings. These results point out that LTE provides a positioning accuracy that complies with a great number of location-based services in LOS. Nevertheless, applications that demand ubiquitous localization may not be correctly carried out in NLOS settings.

To tackle the NLOS issue, we implement UbiLocate, a Wi-Fi location system that copes well with common AP deployment densities and works ubiquitously, i.e., without excessive degradation under NLOS. UbiLocate demonstrates that meter-level median accuracy NLOS localization is possible through (i) an innovative angle estimator based on a Nelder-Mead search, (ii) a fine-grained time of flight ranging system with nanosecond resolution, and (iii) the accuracy improvements brought about by the increase in bandwidth and number of antennas of IEEE 802.11ac. In combination, they provide superior resolvability of multipath components, significantly improving location accuracy over prior work. We implement our location system on off-the-shelf 802.11ac devices. Our experimental evaluation shows an overall improvement of the localization performance by a factor of 2-3.

The latest generation of Wi-Fi standards, IEEE 802.11ax, brings new hardware capabilities that improve the performance of localization and sensing systems. In particular, the 160MHz of channel bandwidth and the four times denser spectrum significantly improve the resolvability of the multipath components compared to its predecessor, 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 out a preliminary measurement campaign to compare the localization accuracy of IEEE 802.11ax with 802.11ac. Our results show that, as expected, IEEE 802.11ax provides superior performance improving the accuracy by a factor of 1.75 for LOS and NLOS settings.

Sensing research goes beyond localization since it aims at providing context awareness. We explore the integration of the proposed multipath decomposition algorithm as well as the testbed for sensing applications. In particular, we tackle human respiration rate estimation since it is appealing as it does not require any specialized hardware. Our results show that an accurate respiration rate estimation is possible by decomposing the channel.

In summary, location-based services demand accurate and ubiquitous localization. However, the state-of-the-art localization systems do not cope well with realistic wireless deployments and their positioning performances drastically degrade in these environments. Hence, we provide a localization framework that copes well with realistic wireless deployments and with NLOS settings. We conclude that resolving accurately the multipath components enables pervasive and precise localization. In addition, sensing enables new applications that are helpful in many issues since it provides not only location but also context awareness. Hence, we show that algorithms and testbeds that are designed for localization can be also utilized for sensing applications by tackling respiration rate estimation.