Localization is envisioned as a key enabler to satisfy the requirements of communications and context-aware services in the fifth/sixth generation (5G/6G) communication systems. User localization can be achieved based on delay and angle estimation using uplink/downlink pilot signals. However, hardware impairments (HWIs) (such as phase noise and mutual coupling) distort the signals at both the transmitter and receiver sides and thus affect the localization performance. While this impact can be ignored at lower frequencies with less severe HWIs, and less stringent localization requirements, modeling and analysis efforts are needed for high-frequency bands to assess degradation in localization accuracy due to HWIs. In this work, we model various types of impairments for a mmWave multiple-input-multiple-output communication system and conduct a misspecified Cramér-Rao bound analysis to evaluate HWI-induced performance losses in terms of angle/delay estimation and the resulting 3D position/orientation estimation error. We also investigate the effect of individual and overall HWIs on communications in terms of symbol error rate (SER). Our extensive simulation results demonstrate that each type of HWI leads to a different level of degradation in angle and delay estimation performance, and the prominent impairment factors on delay estimation will have a dominant negative effect on SER.

Wireless connectivity will soon enable people to consume augmented and virtual reality content anywhere and cloud-connected mobile robots to perform complex tasks collaboratively. This connectivity will come to enterprises, factory floors and digital homes first, powered by indoor networks that offer much higher data rates, greater reliability, and lower latency than today’s networks. In addition to providing traditional communication capabilities, the future indoor network will have a “sixth sense” that enables it to provide sensory information and insights to meet physiological needs such as lighting, heating, health and safety. It will serve as the core infrastructure for smart buildings and help enterprises operate more efficiently by further enabling capabilities such as immersive virtual workplaces, indoor navigation and asset tracking. This paper reviews current technologies, examines the enabling technologies of the future indoor network and presents our latest research results and our vision for implementing them in commercial and residential environments.

In this paper, we study probabilistic time-of-arrival (ToA) and angle-of-arrival (AoA) joint localization in real indoor environments. To mitigate the effects of multipath propagation, the joint localization algorithm incorporates into the likelihood function Gaussian mixture models (GMM) and the Von Mises-Fisher distribution to model time bias errors and angular uncertainty, respectively. We evaluate the algorithm performance using a proprietary prototype deployed in an indoor factory environment with infrastructure receivers in each of the four corners at the ceiling of a 10 meter by 20 meter section. The field test results show that our joint probabilistic localization algorithm significantly outperforms baselines using only ToA or AoA measurements and achieves 2-D sub-meter accuracy at the 90%-ile. We also numerically demonstrate that the joint localization algorithm is more robust to synchronization errors than the baseline using ToA measurements only.

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.

We address the problem of localization in vehicular ad hoc networks. Our goal is to leverage vehicle communications and smartphone sensors to improve the overall localization performance. Assuming vehicles are equipped with the IEEE 802.11p wireless interfaces, we employ a two-stage Bayesian filter to track the vehicle's position: an unscented Kalman filter for heading estimation using smartphone inertial sensors, and a particle filter that fuses vehicle-to-vehicle signal strength measurements received from mobile anchors whose positions are uncertain, with velocity, GPS position, and map information. Our model leads to a robust localization system and is able to provide useful position information even in the absence of GPS data. We evaluate the algorithm performance using real-world measurements collected from four communicating vehicles in an urban scenario, and considering different combinations of location information sources.

Underground mines are characterized by a network of intersecting tunnels and sharp turns, an environment which is particularly challenging for radiofrequency based positioning systems due to extreme multipath, non-line-of-sight propagation, and poor anchor geometry. Such systems typically require a dense grid of devices to enable 3-D positioning. Moreover, the precise position of each anchor node needs to be precisely surveyed, a particularly challenging task in underground environments. Magneto-inductive (MI) positioning, which provides 3-D position and orientation from a single transmitter and penetrates thick layers of soil and rock without loss, is a more promising approach, but so far has only been investigated in simple point-to-point contexts. In this paper, we develop a novel MI positioning approach to cover an extended underground 3-D space with unknown geometry using a rapidly deployable anchor network. The key to our approach is that the position of only a single anchor needs to be accurately surveyed-the positions of all secondary anchors are determined using an iterative refinement process using measurements obtained from receivers within the network. This avoids the particularly challenging and time-intensive task in an underground environment of accurately surveying the positions of all of the transmitters. We also demonstrate how measurements obtained from multiple transmitters can be fused to improve localization accuracy. We validate the proposed approach in a man-made cave and show that, with a portable system that took 5 min to deploy, we were able to provide accurate through-the-earth location capability to nodes placed along a suite of tunnels.

Ferrous and highly conductive materials distort low-frequency magnetic fields and can significantly increase magnetoinductive positioning errors. In this paper, we use the image theory in order to formulate an analytical channel model for the magnetic field of a quasi-static magnetic dipole positioned above a perfectly conducting half-space. The proposed model can be used to compensate for the distorting effects that metallic reinforcement bars (rebars) within the floor impose on the magnetic field of a magnetoinductive transmitter node in an indoor single-story environment. Good agreement is observed between the analytical solution and numerical solutions obtained from 3-D finite-element simulations. Experimental results indicate that the image theory model shows improvement over the free-space dipole model in estimating positions in the distorted environment, typically reducing positioning errors by 22% in 90% of the cases and 26% in 40% of the cases. No prior information on the geometry of the metallic distorters was available, making this essentially a “blind” technique.

Localization is a research area that, due to its overarching importance as an enabler for higher level services, has attracted a vast amount of research and commercial interest. For the most part, it can be claimed that GPS provides an unparalleled solution for outdoor tracking and navigation. However, the same cannot yet be said about positioning in GPS-denied or challenged environments, such as indoor environments, where obstructions such as floors and walls heavily attenuate or reflect high-frequency radio signals. This has led to a plethora of competing solutions targeted toward a particular application scenario, yielding a fragmented solution landscape. In this paper, we present a fresh approach to 3-D positioning based on the use of very low frequency (kHz) magneto-inductive (MI) fields. The most important property of MI positioning is that obstacles such as walls, floors, and people that heavily impact the performance of competing approaches are largely “transparent” to the quasi-static magnetic fields. MI has a number of challenges to robust operation that distort positions, including the presence of ferrous materials and sensitivity to user rotation. Through signal processing and sensor fusion across multiple system layers, we show how we can overcome these challenges. We showcase its highly accurate 3-D positioning in a number of environments, with positioning accuracy below 0.8 m even in heavily distorted areas.

Many wireless applications require a propagation model that describes the attenuation of the transmitted signal as a function of the distance between devices. Such channel models are derived commonly from signal strength measurements, and assume that the true distances between wireless terminals are known. In practice, however, the true distances may be unavailable or difficult to obtain, for instance in mobile scenarios or in the absence of line-of-sight. These conditions typically occur in forested environments, urban areas, etc. This paper addresses the problem of path loss model parameter estimation in presence of erroneous distance measurements, such as the ones derived from the GPS positions. We provide a model for the uncertainties, and study the impact of distance errors on the estimation of a log-distance channel model. Our main conclusion is that the path loss model can be estimated with a reasonable accuracy from unreliable distances, provided that the measurements are taken at distances beyond a few standard deviations of the GPS positioning error. In case the maximum communication range does not allow such large distances, we provide a method to correct the erroneous channel model. Real-world measurements are used in order to validate our approach.

In this paper we propose practical algorithms for optimization under unitary matrix constraint. This type of constrained optimization is needed in many signal processing applications. Steepest descent and conjugate gradient algorithms on the Lie group of unitary matrices are introduced. They exploit the Lie group properties in order to reduce the computational cost. Simulation examples on signal separation in MIMO systems demonstrate the fast convergence and the ability to satisfy the constraint with high fidelity.