276,185 research outputs found
Cram\'er-Rao Bounds for Holographic Positioning
Multiple antennas arrays play a key role in wireless networks for
communications but also for localization and sensing applications. The use of
large antenna arrays at high carrier frequencies (in the mmWave range) pushes
towards a propagation regime in which the wavefront is no longer plane but
spherical. This allows to infer the position and orientation of a transmitting
source from the received signal without the need of using multiple anchor
nodes, located in known positions. To understand the fundamental limits of
large antenna arrays for localization, this paper combines wave propagation
theory with estimation theory, and computes the Cram\'er-Rao Bound (CRB) for
the estimation of the source position on the basis of the three Cartesian
components of the electric field, observed over a rectangular surface area. The
problem is referred to as holographic positioning and is formulated by taking
into account the radiation angular pattern of the transmitting source, which is
typically ignored in standard signal processing models. We assume that the
source is a Hertzian dipole, and address the holographic positioning problem in
both cases, that is, with and without a priori knowledge of its orientation. To
simplify the analysis and gain further insights, we also consider the case in
which the dipole is located on the line perpendicular to the surface center.
Numerical and asymptotic results are given to quantify the CRBs, and to
quantify the effect of various system parameters on the ultimate estimation
accuracy. It turns out that surfaces of practical size may guarantee a
centimeter-level accuracy in the mmWave bands.Comment: 15 pages, 9 figures, IEEE Transactions on Signal Processin
Cooperative Position and Orientation Estimation with Multi-Mode Antennas
Robotic multi-agent systems are envisioned for planetary exploration and terrestrial applications. Autonomous operation of robots requires estimations of their positions and orientations, which are obtained from the direction-of-arrival (DoA) and the time-of-arrival (ToA) of radio signals exchanged among the agents. In this thesis, we estimate the signal DoA and ToA using a multi-mode antenna (MMA). An MMA is a single antenna element, where multiple orthogonal current modes are excited by different antenna ports. We provide a first study on the use of MMAs for cooperative position and orientation estimation, specifically exploring their DoA estimation capabilities. Assuming the agents of a cooperative network are equipped with MMAs, lower bounds on the achievable position and orientation accuracy are derived. We realize a gap between the theoretical lower bounds and real-world performance of a cooperative radio localization system, which is caused by imperfect antenna and transceiver calibration. Consequentially, we theoretically analyze in-situ antenna calibration, introduce an algorithm for the calibration of arbitrary multiport antennas and show its effectiveness by simulation. To also improve calibration during operation, we propose cooperative simultaneous localization and calibration (SLAC). We show that cooperative SLAC is able to estimate antenna responses and ranging biases of the agents together with their positions and orientations, leading to considerably better position and orientation accuracy. Finally, we validate the results from theory and simulation by experiments with robotic rovers equipped with software-defined radios (SDRs). In conclusion, we show that DoA estimation with an MMA is feasible, and accuracy can be improved by in-situ calibration and SLAC
Multi-Array 5G V2V Relative Positioning: Performance Bounds
We study the performance bounds of vehicle-to-vehicle (V2V) relative
positioning for vehicles with multiple antenna arrays. The Cram\'{e}r-Rao bound
for the estimation of the relative position and the orientation of the Tx
vehicle is derived, when angle of arrival (AOA) measurements with or without
time-difference of arrival (TDOA) measurements are used. In addition,
geometrically intuitive expressions for the corresponding Fisher information
are provided. The derived bounds are numerically evaluated for different
carrier frequencies, bandwidths and array configurations under different V2V
scenarios, i.e. overtaking and platooning. The significance of the AOA and TDOA
measurements for position estimation is investigated. The achievable
positioning accuracy is then compared with the present requirements of the 3rd
Generation Partnership Project (3GPP) 5G New Radio (NR) vehicle-to-everything
(V2X) standardization
Understanding the Limitations of CNN-based Absolute Camera Pose Regression
Visual localization is the task of accurate camera pose estimation in a known
scene. It is a key problem in computer vision and robotics, with applications
including self-driving cars, Structure-from-Motion, SLAM, and Mixed Reality.
Traditionally, the localization problem has been tackled using 3D geometry.
Recently, end-to-end approaches based on convolutional neural networks have
become popular. These methods learn to directly regress the camera pose from an
input image. However, they do not achieve the same level of pose accuracy as 3D
structure-based methods. To understand this behavior, we develop a theoretical
model for camera pose regression. We use our model to predict failure cases for
pose regression techniques and verify our predictions through experiments. We
furthermore use our model to show that pose regression is more closely related
to pose approximation via image retrieval than to accurate pose estimation via
3D structure. A key result is that current approaches do not consistently
outperform a handcrafted image retrieval baseline. This clearly shows that
additional research is needed before pose regression algorithms are ready to
compete with structure-based methods.Comment: Initial version of a paper accepted to CVPR 201
On Reliability of Underwater Magnetic Induction Communications with Tri-Axis Coils
Underwater magnetic induction communications (UWMICs) provide a low-power and
high-throughput solution for autonomous underwater vehicles (AUVs), which are
envisioned to explore and monitor the underwater environment. UWMIC with
tri-axis coils increases the reliability of the wireless channel by exploring
the coil orientation diversity. However, the UWMIC channel is different from
typical fading channels and the mutual inductance information (MII) is not
always available. It is not clear the performance of the tri-axis coil MIMO
without MII. Also, its performances with multiple users have not been
investigated. In this paper, we analyze the reliability and multiplexing gain
of UWMICs with tri-axis coils by using coil selection. We optimally select the
transmit and receive coils to reduce the computation complexity and power
consumption and explore the diversity for multiple users. We find that without
using all the coils and MII, we can still achieve reliability. Also, the
multiplexing gain of UWMIC without MII is 5dB smaller than typical terrestrial
fading channels. The results of this paper provide a more power-efficient way
to use UWMICs with tri-axis coils
Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays
Massive MIMO (multiple-input multiple-output) is no longer a "wild" or
"promising" concept for future cellular networks - in 2018 it became a reality.
Base stations (BSs) with 64 fully digital transceiver chains were commercially
deployed in several countries, the key ingredients of Massive MIMO have made it
into the 5G standard, the signal processing methods required to achieve
unprecedented spectral efficiency have been developed, and the limitation due
to pilot contamination has been resolved. Even the development of fully digital
Massive MIMO arrays for mmWave frequencies - once viewed prohibitively
complicated and costly - is well underway. In a few years, Massive MIMO with
fully digital transceivers will be a mainstream feature at both sub-6 GHz and
mmWave frequencies. In this paper, we explain how the first chapter of the
Massive MIMO research saga has come to an end, while the story has just begun.
The coming wide-scale deployment of BSs with massive antenna arrays opens the
door to a brand new world where spatial processing capabilities are
omnipresent. In addition to mobile broadband services, the antennas can be used
for other communication applications, such as low-power machine-type or
ultra-reliable communications, as well as non-communication applications such
as radar, sensing and positioning. We outline five new Massive MIMO related
research directions: Extremely large aperture arrays, Holographic Massive MIMO,
Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive
MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin
Effects of nongaussian diffusion on "isotropic diffusion measurements'': an ex-vivo microimaging and simulation study
Designing novel diffusion-weighted pulse sequences to probe tissue
microstructure beyond the conventional Stejskal-Tanner family is currently of
broad interest. One such technique, multidimensional diffusion MRI, has been
recently proposed to afford model-free decomposition of diffusion signal
kurtosis into terms originating from either ensemble variance of isotropic
diffusivity or microscopic diffusion anisotropy. This ability rests on the
assumption that diffusion can be described as a sum of multiple Gaussian
compartments, but this is often not strictly fulfilled. The effects of
nongaussian diffusion on single shot isotropic diffusion sequences were first
considered in detail by de Swiet and Mitra in 1996. They showed theoretically
that anisotropic compartments lead to anisotropic time dependence of the
diffusion tensors, which causes the measured isotropic diffusivity to depend on
gradient frame orientation. Here we show how such deviations from the multiple
Gaussian compartments assumption conflates orientation dispersion with ensemble
variance in isotropic diffusivity. Second, we consider additional contributions
to the apparent variance in isotropic diffusivity arising due to
intracompartmental kurtosis. These will likewise depend on gradient frame
orientation. We illustrate the potential importance of these confounds with
analytical expressions, numerical simulations in simple model geometries, and
microimaging experiments in fixed spinal cord using isotropic diffusion
encoding waveforms with 7.5 ms duration and 3000 mT/m maximum amplitude.Comment: 26 pages, 9 figures. Appearing in J. Magn. Reso
A Network Coding Approach to Loss Tomography
Network tomography aims at inferring internal network characteristics based
on measurements at the edge of the network. In loss tomography, in particular,
the characteristic of interest is the loss rate of individual links and
multicast and/or unicast end-to-end probes are typically used. Independently,
recent advances in network coding have shown that there are advantages from
allowing intermediate nodes to process and combine, in addition to just
forward, packets. In this paper, we study the problem of loss tomography in
networks with network coding capabilities. We design a framework for estimating
link loss rates, which leverages network coding capabilities, and we show that
it improves several aspects of tomography including the identifiability of
links, the trade-off between estimation accuracy and bandwidth efficiency, and
the complexity of probe path selection. We discuss the cases of inferring link
loss rates in a tree topology and in a general topology. In the latter case,
the benefits of our approach are even more pronounced compared to standard
techniques, but we also face novel challenges, such as dealing with cycles and
multiple paths between sources and receivers. Overall, this work makes the
connection between active network tomography and network coding
Position and Orientation Estimation through Millimeter Wave MIMO in 5G Systems
Millimeter wave signals and large antenna arrays are considered enabling
technologies for future 5G networks. While their benefits for achieving
high-data rate communications are well-known, their potential advantages for
accurate positioning are largely undiscovered. We derive the Cram\'{e}r-Rao
bound (CRB) on position and rotation angle estimation uncertainty from
millimeter wave signals from a single transmitter, in the presence of
scatterers. We also present a novel two-stage algorithm for position and
rotation angle estimation that attains the CRB for average to high
signal-to-noise ratio. The algorithm is based on multiple measurement vectors
matching pursuit for coarse estimation, followed by a refinement stage based on
the space-alternating generalized expectation maximization algorithm. We find
that accurate position and rotation angle estimation is possible using signals
from a single transmitter, in either line-of- sight, non-line-of-sight, or
obstructed-line-of-sight conditions.Comment: The manuscript has been revised, and increased from 27 to 31 pages.
Also, Fig.2, Fig. 10 and Table I are adde
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