190 research outputs found
Wireless capsule gastrointestinal endoscopy: direction of arrival estimation based localization survey
One of the significant challenges in Capsule Endoscopy (CE) is to precisely determine the pathologies location. The localization process is primarily estimated using the received signal strength from sensors in the capsule system through its movement in the gastrointestinal (GI) tract. Consequently, the wireless capsule endoscope (WCE) system requires improvement to handle the lack of the capsule instantaneous localization information and to solve the relatively low transmission data rate challenges. Furthermore, the association between the capsule’s transmitter position, capsule location, signal reduction and the capsule direction should be assessed. These measurements deliver significant information for the instantaneous capsule localization systems based on TOA (time of arrival) approach, PDOA (phase difference of arrival), RSS (received signal strength), electromagnetic, DOA (direction of arrival) and video tracking approaches are developed to locate the WCE precisely. The current article introduces the acquisition concept of the GI medical images using the endoscopy with a comprehensive description of the endoscopy system components. Capsule localization and tracking are considered to be the most important features of the WCE system, thus the current article emphasizes the most common localization systems generally, highlighting the DOA-based localization systems and discusses the required significant research challenges to be addressed
Dominant Channel Estimation via MIPS for Large-Scale Antenna Systems with One-Bit ADCs
In large-scale antenna systems, using one-bit analog-to-digital converters
(ADCs) has recently become important since they offer significant reductions in
both power and cost. However, in contrast to high-resolution ADCs, the coarse
quantization of one-bit ADCs results in an irreversible loss of information. In
the context of channel estimation, studies have been developed extensively to
combat the performance loss incurred by one-bit ADCs. Furthermore, in the field
of array signal processing, direction-of-arrival (DOA) estimation combined with
one-bit ADCs has gained growing interests recently to minimize the estimation
error. In this paper, a channel estimator is proposed for one-bit ADCs where
the channels are characterized by their angular geometries, e.g., uniform
linear arrays (ULAs). The goal is to estimate the dominant channel among
multiple paths. The proposed channel estimator first finds the DOA estimate
using the maximum inner product search (MIPS). Then, the channel fading
coefficient is estimated using the concavity of the log-likelihood function.
The limit inherent in one-bit ADCs is also investigated, which results from the
loss of magnitude information.Comment: to appear in GLOBECOM 2018, Abu Dhabi, UA
Sparse RF Lens Antenna Array Design for AoA Estimation in Wideband Systems: Placement Optimization and Performance Analysis
In this paper, we propose a novel architecture for a lens antenna array (LAA)
designed to work with a small number of antennas and enable angle-of-arrival
(AoA) estimation for advanced 5G vehicle-to-everything (V2X) use cases that
demand wider bandwidths and higher data rates. We derive a received signal in
terms of optical analysis to consider the variability of the focal region for
different carrier frequencies in a wideband multi-carrier system. By taking
full advantage of the beam squint effect for multiple pilot signals with
different frequencies, we propose a novel reconfiguration of antenna array
(RAA) for the sparse LAA and a max-energy antenna selection (MS) algorithm for
the AoA estimation. In addition, this paper presents an analysis of the
received power at the single antenna with the maximum energy and compares it to
simulation results. In contrast to previous studies on LAA that assumed a large
number of antennas, which can require high complexity and hardware costs, the
proposed RAA with MS estimation algorithm is shown meets the requirements of 5G
V2X in a vehicular environment while utilizing limited RF hardware and has low
complexity.Comment: 15 pages, 10 figure
AoA-based Position and Orientation Estimation Using Lens MIMO in Cooperative Vehicle-to-Vehicle Systems
Positioning accuracy is a critical requirement for vehicle-to-everything
(V2X) use cases. Therefore, this paper derives the theoretical limits of
estimation for the position and orientation of vehicles in a cooperative
vehicle-to-vehicle (V2V) scenario, using a lens-based multiple-input
multiple-output (lens-MIMO) system. Following this, we analyze the
Cramr-Rao lower bounds (CRLBs) of the position and
orientation estimation and explore a received signal model of a lens-MIMO for
the particular angle of arrival (AoA) estimation with a V2V geometric model.
Further, we propose a lower complexity AoA estimation technique exploiting the
unique characteristics of the lens-MIMO for a single target vehicle; as a
result, its estimation scheme is effectively extended by the successive
interference cancellation (SIC) method for multiple target vehicles. Given
these AoAs, we investigate the lens-MIMO estimation capability for the
positions and orientations of vehicles. Subsequently, we prove that the
lens-MIMO outperforms a conventional uniform linear array (ULA) in a certain
configuration of a lens's structure. Finally, we confirm that the proposed
localization algorithm is superior to ULA's CRLB as the resolution of the lens
increases in spite of the lower complexity.Comment: 16 pages, 11 figure
Image formation in synthetic aperture radio telescopes
Next generation radio telescopes will be much larger, more sensitive, have
much larger observation bandwidth and will be capable of pointing multiple
beams simultaneously. Obtaining the sensitivity, resolution and dynamic range
supported by the receivers requires the development of new signal processing
techniques for array and atmospheric calibration as well as new imaging
techniques that are both more accurate and computationally efficient since data
volumes will be much larger. This paper provides a tutorial overview of
existing image formation techniques and outlines some of the future directions
needed for information extraction from future radio telescopes. We describe the
imaging process from measurement equation until deconvolution, both as a
Fourier inversion problem and as an array processing estimation problem. The
latter formulation enables the development of more advanced techniques based on
state of the art array processing. We demonstrate the techniques on simulated
and measured radio telescope data.Comment: 12 page
Antenna Systems
This book offers an up-to-date and comprehensive review of modern antenna systems and their applications in the fields of contemporary wireless systems. It constitutes a useful resource of new material, including stochastic versus ray tracing wireless channel modeling for 5G and V2X applications and implantable devices. Chapters discuss modern metalens antennas in microwaves, terahertz, and optical domain. Moreover, the book presents new material on antenna arrays for 5G massive MIMO beamforming. Finally, it discusses new methods, devices, and technologies to enhance the performance of antenna systems
A Framework for Developing and Evaluating Algorithms for Estimating Multipath Propagation Parameters from Channel Sounder Measurements
A framework is proposed for developing and evaluating algorithms for
extracting multipath propagation components (MPCs) from measurements collected
by channel sounders at millimeter-wave frequencies. Sounders equipped with an
omnidirectional transmitter and a receiver with a uniform planar array (UPA)
are considered. An accurate mathematical model is developed for the spatial
frequency response of the sounder that incorporates the non-ideal cross-polar
beampatterns for the UPA elements. Due to the limited Field-of-View (FoV) of
each element, the model is extended to accommodate multi-FoV measurements in
distinct azimuth directions. A beamspace representation of the spatial
frequency response is leveraged to develop three progressively complex
algorithms aimed at solving the singlesnapshot maximum likelihood estimation
problem: greedy matching pursuit (CLEAN), space-alternative generalized
expectationmaximization (SAGE), and RiMAX. The first two are based on purely
specular MPCs whereas RiMAX also accommodates diffuse MPCs. Two approaches for
performance evaluation are proposed, one with knowledge of ground truth
parameters, and one based on reconstruction mean-squared error. The three
algorithms are compared through a demanding channel model with hundreds of MPCs
and through real measurements. The results demonstrate that CLEAN gives quite
reasonable estimates which are improved by SAGE and RiMAX. Lessons learned and
directions for future research are discussed.Comment: 17 page
- …