Indoor positioning is a major challenge for location-based services. WiFi deployments are often used to address indoor positioning. Yet, they requiremultiple access points, which may not be available or accessible for localization in all scenarios, or they make unrealistic assumptions for practical deployments. In this paper we presentSPRING+, a positioning system that extracts and processes Channel State Information (CSI) and Fine Time Measurements (FTM) from a single Access Point (AP) to localize commercial smartphones. First, we propose an adaptive method for estimating the Angle of Arrival (AOA) from CSI that works on single packets and leverages information from the estimated number of paths. Second, we present a new method to detect the first path using FTM measurements, robust to multipath scenarios. We evaluate SPRING+ in an extensive experimental campaign consisting of four different testbeds: i) generic indoor spaces, ii) generic indoor spaces with obstacles, iii) office environments and iv) home environments. Our results show that SPRING+ is able to achieve a median 2D positioning error between 1 and 1.8 meters with asingle WiFi AP.

Every year millions of lives are lost in emergency situations. Finding missing people in the shortest possible time is the most effective tool to reduce such a death toll. However, this is challenging when victims are unable to communicate by themselves, located in large areas and/or difficult to reach. In this paper we present 3DSAR, a single-drone 3D cellular search-and-rescue solution, leveraging on 5G-new radio (NR), able to estimate the location of people through their mobile phones. Our novel 3DSAR design consists of 4 main components to improve the localization accuracy of state-of-the-art drone-based cellular localization solutions: i) distance estimator, ii) angles estimator, iii) 3D positioning, and iv) 3D unnamed aerial vehicles (UAV) prediction trajectory algorithm. Our results show that 3DSAR improves by an order of magnitude the positioning error of current single-drone solutions thanks to its dynamic 3D trajectory control.

The advent of the fourth Industrial Revolution (Industry 4.0) requires wireless networked solutions to connect machines. However, the industrial environment is notorious for being averse to wireless communication, with traditional wireless resource mechanisms prone to errors because of metallic objects. In this work, we propose to exploit the knowledge of location to derive context information and dynamically allocate wireless resources intime and spaceto target devices. We exploit the spatial geometry of the Access Points (APs) and we introduce a statistical model that maps the user position’s spatial distribution to an angle error distribution and derive a hypothesis test to declare if the link is under metallic blockage or not. In order to avoid changes to the client side and operate with a single interface radio, we usethe samewireless network both for positioning and scheduling. We experimentally show that our system can localize four mobile robots deployed in a very harsh environment with metal obstacles and reflections. Context information applied to wireless resources protocol help increasing up to 40 percent of the network throughput in the above industrial-like scenario.

Inertial sensors embedded in mobile devices, such as accelerometers and gyroscopes, have shown great potential to study human motion. In this paper, we propose to estimate the device movement without any access to physical inertial sensors in the mobile. Our 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 rotation of a user that uses only WiFi Fine Time Measurements (FTM) to infer the rotation speed. We evaluate and demonstrate the proposed approach with experiments, using commodity 802.11ac Access Point (AP)s for Channel State Information (CSI) and FTMs measurements, and a Google Pixel 3 smartphone as mobile terminal. 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 is limited by the typical one single WiFi antenna at the smartphone side. Together with walking speed estimation of a user, we envision that virtual inertial sensors can be leveraged by location systems and sensing mechanisms, including 5G, to improve localization accuracy, infer user behavior, and design better and more secure communication.

Despite the large literature on localization, there is no solution yet to localize a commercial off-the-shelf smartphone device using radio measurements from a single WiFi AP. We present SPRING, Smartphone Positioning with Radio measurements from a sINGle wifi access point. SPRING exploits Fine Time Measurements (FTM) and Angle of Arrival (AOA) extracted from commercial chipsets exploiting the specifications of the recent 802.11-2016 and the 802.11ac amendment to combine distance and direction from the AP to the client for positioning. Our system has the potential to bring indoor positioning to homes and small businesses which typically have a single access point. We exploit physical layer (PHY) information to detect the number of paths and their directions. 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 Channel State Information (CSI). Our evaluation in indoor scenarios in multipath rich environments demonstrates that the combination of AOA estimation and the proposed FTM refinement approach can locate a Google Pixel 3 smartphone with a median positioning error of 0.9-2.15 m through an area comparable to typical flat sizes.

Time-of-flight (ToF) echo techniques have been recently suggested for ranging mobile devices over WiFi radios. However, these techniques have yielded only moderate accuracy in indoor environments because WiFi ToF measurements 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 device-related Gaussian noise is in the same order of magnitude, or larger than the multipath noise. In order to address this challenge, we propose a new method for filtering ranging measurements that is better suited for the inherent large noise as found in WiFi radios. Our technique combines statistical learning and robust statistics in a single filter. The filter is lightweight in the sense that it does not require specialized hardware, the intervention of the user, or cumbersome on-site manual calibration. This makes our method particularly suitable for indoor localization in large-scale deployments using existing legacy WiFi infrastructures. We evaluate our technique for indoor mobile tracking scenarios in multipath environments and, through extensive evaluations across four different testbeds covering areas up to 1000m2, the filter is able to achieve a median 2-D positioning error between 2 and 3.4 m.

Time-of-Flight (ToF) echo techniques have been proposed as a way to estimate the range between regular Wi-Fi stations. Recent works either did not address practical questions for deployability, or made evaluations in basic setups, or used advanced 802.11 hardware designs. We build an approach solely deployed using ToF measurements and relying on software access point (AP) upgrades of simple commercial off-the-shelf 802.11 chipsets. Our solution filters noisy measurements collected by WiFi chipsets of six dollars each, it has been tested across different and heterogeneous setups and testbeds, and has the potential to enable ToF ranging in every Wi-Fi chipsets.