536 research outputs found
Sparse Bases and Bayesian Inference of Electromagnetic Scattering
Many approaches in CEM rely on the decomposition of complex radiation and scattering behavior with a set of basis vectors. Accurate estimation of the quantities of interest can be synthesized through a weighted sum of these vectors. In addition to basis decompositions, sparse signal processing techniques developed in the CS community can be leveraged when only a small subset of the basis vectors are required to sufficiently represent the quantity of interest. We investigate several concepts in which novel bases are applied to common electromagnetic problems and leverage the sparsity property to improve performance and/or reduce computational burden. The first concept explores the use of multiple types of scattering primitives to reconstruct scattering patterns of electrically large targets. Using a combination of isotropic point scatterers and wedge diffraction primitives as our bases, a 40% reduction in reconstruction error can be achieved. Next, a sparse basis is used to improve DOA estimation. We implement the BSBL technique to determine the angle of arrival of multiple incident signals with only a single snapshot of data from an arbitrary arrangement of non-isotropic antennas. This is an improvement over the current state-of-the-art, where restrictions on the antenna type, configuration, and a priori knowledge of the number of signals are often assumed. Lastly, we investigate the feasibility of a basis set to reconstruct the scattering patterns of electrically small targets. The basis is derived from the TCM and can capture non-localized scattering behavior. Preliminary results indicate that this basis may be used in an interpolation and extrapolation scheme to generate scattering patterns over multiple frequencies
Location and Orientation Optimisation for Spatially Stretched Tripole Arrays Based on Compressive Sensing
The design of sparse spatially stretched tripole
arrays is an important but also challenging task and this paper
proposes for the very first time efficient solutions to this problem.
Unlike for the design of traditional sparse antenna arrays, the
developed approaches optimise both the dipole locations and
orientations. The novelty of the paper consists in formulating
these optimisation problems into a form that can be solved by the
proposed compressive sensing and Bayesian compressive sensing
based approaches. The performance of the developed approaches
is validated and it is shown that accurate approximation of a
reference response can be achieved with a 67% reduction in the
number of dipoles required as compared to an equivalent uniform
spatially stretched tripole array, leading to a significant reduction
in the cost associated with the resulting arrays
Near-Field Communications: A Comprehensive Survey
Multiple-antenna technologies are evolving towards large-scale aperture
sizes, extremely high frequencies, and innovative antenna types. This evolution
is giving rise to the emergence of near-field communications (NFC) in future
wireless systems. Considerable attention has been directed towards this
cutting-edge technology due to its potential to enhance the capacity of
wireless networks by introducing increased spatial degrees of freedom (DoFs) in
the range domain. Within this context, a comprehensive review of the state of
the art on NFC is presented, with a specific focus on its 1) fundamental
operating principles, 2) channel modeling, 3) performance analysis, 4) signal
processing, and 5) integration with other emerging technologies. Specifically,
1) the basic principles of NFC are characterized from both physics and
communications perspectives, unveiling its unique properties in contrast to
far-field communications. 2) Based on these principles, deterministic and
stochastic near-field channel models are investigated for spatially-discrete
(SPD) and continuous-aperture (CAP) antenna arrays. 3) Rooted in these models,
existing contributions on near-field performance analysis are reviewed in terms
of DoFs/effective DoFs (EDoFs), power scaling law, and transmission rate. 4)
Existing signal processing techniques for NFC are systematically surveyed,
encompassing channel estimation, beamforming design, and low-complexity beam
training. 5) Major issues and research opportunities associated with the
integration of NFC and other emerging technologies are identified to facilitate
NFC applications in next-generation networks. Promising directions are
highlighted throughout the paper to inspire future research endeavors in the
realm of NFC.Comment: 56 pages, 23figures; submit for possible journa
Localization and Tracking of Intestinal Paths for Wireless Capsule Endoscopy
Wireless capsule endoscopy (WCE) is a non-invasive technology used for visual inspection of the human gastrointestinal (GI) tract. Localization of the capsule is a vital component of the system, as this enables physicians to identify the position of abnormalities. Several approaches exist that use the received signal strength (RSS) of the radio frequency (RF) signals for localization. However, few of these utilize the sparseness of the signals. Due to intestinal motility, the capsule positions will change with time. The distance travelled by the capsule in the intestine, however, remains more or less constant with time. In this thesis, a compressive sensing (CS) based localization algorithm is presented, that utilize signal sparsity in the RSS measurements. Different L1-minimization algorithms are used to find the sparse location vector. The performance is evaluated by electromagnetic (EM) simulations performed on a human voxel model, using narrow-band (NB) and ultra wide-band (UWB) signals. From intestinal positions, the distance the capsule has travelled is estimated by use of Kalman- and particle filters. It was found that localization accuracy of a few millimeters is possible under ideal conditions, when the RSS measurements are generated from a path loss model. When using path loss data from the EM simulations, localization accuracy on the order of 20-30 mm was achievable for NB signals. Use of UWB signals resulted in localization errors between 35-60 mm, depending on frequency range and bandwidth. From generated intestinal positions, the travelled distance was estimated with a minimum accuracy of a few millimeters, when using a VNL Kalman filter and moderate amounts of observation noise. The results are found from a limited amount of data. In order to increase the confidence in the presented results, the performance of the localization algorithm and the filters should be evaluated with a larger number of datasets
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