Positioning is an enabler for many location-based services in the vertical industrial domain such as autonomous driving and robot-to-cobot interaction, to name a few. Industry and academia are actively enabling localization through 5G and WiFi as they are universally deployed. However, the current WiFi and 5G protocols cannot deal with the hard requirements of accuracy, latency, and robustness. To deal with that, we have proposed a series of works to enable precise, pervasive, and real-time positioning.

This paper presents the design, implementation and evaluation of waveSLAM, a low-cost mobile robot system that uses the millimetre wave (mmWave) communication devices to enhance the indoor mapping process targeting environments with reduced visibility or glass/mirror walls. A unique feature of waveSLAM is that it only leverages existing Commercial-Off-The-Shelf (COTS) hardware (Lidar and mmWave radios) that are mounted on mobile robots to improve the accurate indoor mapping achieved with optical sensors. The key intuition behind the waveSLAM design is that while the mobile robots moves freely, the mmWave radios can periodically exchange angle and distance estimates between themselves (self-sensing) by bouncing the signal from the environment, thus enabling accurate estimates of the target object/material surface. Our experiments verify that waveSLAM can archive cm-level accuracy with errors below 22 cm and 20° in angle orientation which is compatible with Lidar when building indoor maps.

Thanks to the definition of the new IEEE 802.11bf standard, the development of Wi-Fi sensing applications is gaining momentum in the research community. In this regard, several studies have shown that learning-based approaches that leverage the frequency response of the Wi-Fi channel in the sub-7 GHz bands can reach high accuracy in different classification tasks, such as activity recognition, or person identification. Instead, more fine-grained applications – e.g., human localization and tracking, or respiration and heartbeat monitoring – require implementing model-based approaches to estimate the Wi-Fi multi-path parameters and analyze the time evolution of the paths associated with specific targets (the human body or chest). In this paper, we investigate the performance of six super-resolution algorithms for sub-7 GHz multi-path parameter estimation. Our extensive evaluation indicates that the estimation accuracy that can be achieved through commercial devices allows implementing human localization and tracking strategies but is insufficient to effectively design human vital signs monitoring applications due to the limited frequency and spatial diversity. We pledge to release our implementations for further investigations.1

Millimeter-wave (mmWave) technology is an important element to increase the throughput and reduce latency of future wireless networks. At the same time, its high bandwidth and highly directional antennas allow for unprecedented accuracy in wireless sensing and localization applications. In this paper, we thoroughly analyze mmWave localization and find that it is either extremely accurate or has a very high error, since there is significant mmWave coverage via reflections and even through walls. As a consequence, sub-6 GHz technology can not only provide (coarse) localization where mmWave is not available, but is also critical to decide among multiple candidate antennas and APs for accurate mmWave localization.

WiFi location systems are remarkably accurate, with decimeter-level errors for recent CSI-based systems. However, such high accuracy is achieved under Line-of-Sight (LOS) conditions and with an access point (AP) density that is much higher than that typically found in current deployments that primarily target good coverage. In contrast, when many of the APs within range are in Non-Line-of-Sight (NLOS), the location accuracy degrades drastically. In this paper we present UbiLocate, a WiFi 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 and make the implementation, CSI-extraction tool and custom Fine Timing Measurement design publicly available to the research community. We carry out an extensive performance analysis of our system and show that it outperforms current state-of-the-art location systems by a factor of 2--3, both under LOS and NLOS.

Precise localization is becoming an integral part of mobile network architectures, not only to provide location-based services but also to optimize the operation of the network itself through suitable context information. Location systems are of particular importance for indoor settings where GPS may be unavailable. While upcoming 5G systems will provide improved location accuracy, for a long time to come many areas will only have LTE coverage, and ubiquitous localization will thus also have to rely on LTE technology. To evaluate the location accuracy that can be achieved with current mobile systems, we implement a localization algorithm in a standard-compliant LTE testbed based on software-defined radios. We assess the localization accuracy in representative indoor scenarios. Despite a bandwidth of only 20MHz, the results show a good median error around 2m, but significantly larger errors may occur in non-line-of-sight cases. Nevertheless, the accuracy is sufficient for a range of potential applications.