GLRT-based threshold detection-estimation performance improvement and application to uniform circular antenna arrays

©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE."This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."The problem of estimating the number of independent Gaussian sources and their parameters impinging upon an antenna array is addressed for scenarios that are problematic for standard techniques, namely, under "threshold conditions" (where subspace techniques such as MUSIC experience an abrupt and dramatic performance breakdown). We propose an antenna geometry-invariant method that adopts the generalized-likelihood-ratio test (GLRT) methodology, supported by a maximum-likelihood-ratio lower-bound analysis that allows erroneous solutions ("outliers") to be found and rectified. Detection-estimation performance in both uniform circular and linear antenna arrays is shown to be significantly improved compared with conventional techniques but limited by the performance-breakdown phenomenon that is intrinsic to all such maximum-likelihood (ML) techniques.Yuri I. Abramovich, Nicholas K. Spencer, and Alexei Y. Gorokho

Source enumeration via Toeplitz matrix completion

This paper addresses the problem of source enumeration by an array of sensors in the presence of noise whose spatial covariance structure is a diagonal matrix with possibly different variances, referred to non-iid noise hereafter, when the sources are uncorrelated. The diagonal terms of the sample covariance matrix are removed and, after applying Toeplitz rectification as a denoising step, the signal covariance matrix is reconstructed by using a low-rank matrix completion method adapted to enforce the Toeplitz structure of the sought solution. The proposed source enumeration criterion is based on the Frobenius norm of the reconstructed signal covariance matrix obtained for increasing rank values. As illustrated by simulation examples, the proposed method performs robustly for both small and large-scale arrays with few snapshots, i.e. small-sample regime.This work was supported by the Ministerio de Economía y Competitividad (MINECO) of Spain, and AEI/FEDER funds of the E.U., under grants TEC2016-75067-C4-4-R (CARMEN), PID2019-104958RB-C43/C41 (ADELE) and BES-2017-080542

Unit Circle Roots Based Sensor Array Signal Processing

As technology continues to rapidly evolve, the presence of sensor arrays and the algorithms processing the data they generate take an ever-increasing role in modern human life. From remote sensing to wireless communications, the importance of sensor signal processing cannot be understated. Capon\u27s pioneering work on minimum variance distortionless response (MVDR) beamforming forms the basis of many modern sensor array signal processing (SASP) algorithms. In 2004, Steinhardt and Guerci proved that the roots of the polynomial corresponding to the optimal MVDR beamformer must lie on the unit circle, but this result was limited to only the MVDR. This dissertation contains a new proof of the unit circle roots property which generalizes to other SASP algorithms. Motivated by this result, a unit circle roots constrained (UCRC) framework for SASP is established and includes MVDR as well as single-input single-output (SISO) and distributed multiple-input multiple-output (MIMO) radar moving target detection. Through extensive simulation examples, it will be shown that the UCRC-based SASP algorithms achieve higher output gains and detection probabilities than their non-UCRC counterparts. Additional robustness to signal contamination and limited secondary data will be shown for the UCRC-based beamforming and target detection applications, respectively

Real-time Microphone Array Processing for Sound-field Analysis and Perceptually Motivated Reproduction

This thesis details real-time implementations of sound-field analysis and perceptually motivated reproduction methods for visualisation and auralisation purposes. For the former, various methods for visualising the relative distribution of sound energy from one point in space are investigated and contrasted; including a novel reformulation of the cross-pattern coherence (CroPaC) algorithm, which integrates a new side-lobe suppression technique. Whereas for auralisation applications, listening tests were conducted to compare ambisonics reproduction with a novel headphone formulation of the directional audio coding (DirAC) method. The results indicate that the side-lobe suppressed CroPaC method offers greater spatial selectivity in reverberant conditions compared with other popular approaches, and that the new DirAC formulation yields higher perceived spatial accuracy when compared to the ambisonics method

Hybrid THz architectures for molecular polaritonics

Physical and chemical properties of materials can be modified by a resonant optical mode. Such recent demonstrations have mostly relied on a planar cavity geometry, others have relied on a plasmonic resonator. However, the combination of these two device architectures have remained largely unexplored, especially in the context of maximizing light-matter interactions. Here, we investigate several schemes of electromagnetic field confinement aimed at facilitating the collective coupling of a localized photonic mode to molecular vibrations in the terahertz region. The key aspects are the use of metasurface plasmonic structures combined with standard Fabry-Perot configurations and the deposition of a thin layer of glucose, via a spray coating technique, within a tightly focused electromagnetic mode volume. More importantly, we demonstrate enhanced vacuum Rabi splittings reaching up to 200 GHz when combining plasmonic resonances, photonic cavity modes and low-energy molecular resonances. Furthermore, we demonstrate how a cavity mode can be utilized to enhance the zero-point electric field amplitude of a plasmonic resonator. Our study provides key insight into the design of polaritonic platforms with organic molecules to harvest the unique properties of hybrid light-matter states.Comment: 7 pages (5 Figures) + 7 pages Appendix (5 Figures), updated versio

Probing Radiative Thermal Transport at the Nanoscale.

Thermal radiative emission from a hot to a cold surface plays an important role in many applications, including energy conversion, thermal management, lithography, data storage, and thermal microscopy. While thermal radiation at length scales larger than the dominant wavelength is well understood in terms of Planck’s law and the Stefan-Boltzmann law, near-field thermal radiation is not. With constantly advancing micro- and nanofabrication techniques and ever smaller devices a substantial need for a better and more reliable understanding of the fundamental physics governing nanoscale radiative heat transfer has arisen. Unfortunately, and in stark contrast to the abundance of theoretical and numerical work, there have only been limited experimental efforts and achievements. The central challenge in the field is to accurately and unambiguously characterize radiative heat transport between well-defined surfaces across nanometer distances. The key scientific and technological questions that I have experimentally addressed during my doctoral study include: How does radiative heat transfer between an emitter and a receiver depend on their spatial separation (gap size), and does the radiative heat flux increase by over five orders of magnitude as the gap size is reduced to a few nanometers, as theoretically predicted? Can polar dielectric and metallic thin films support substantial near-field heat flow enhancement? For single-digit nanometer gaps, is the widely used theoretical framework of fluctuational electrodynamics (still) applicable? To address these challenging questions in gap sizes as small as tens of nanometers, we developed a nanopositioning platform to precisely control the gap between a microfabricated emitter device and a suspended receiver/calorimeter device which enables simultaneous measurement of the radiative heat flow across the gap. Further, we employed an atomic force microscope (AFM) in conjunction with stiff custom-fabricated scanning thermal microscopy (SThM) probes to explore the extreme near-field characterized by gaps of a few nanometers. In both approaches, high vacuum, vibration isolation and temperature control are implemented for accurate thermal measurements and for maintaining a stable gap. Finally, we performed state-of-the-art fluctuational electrodynamics-based calculations and analysis to compare theoretical predictions with experimental observations.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116634/1/baisong_1.pd

Subspace-based order estimation techniques in massive MIMO

Order estimation, also known as source enumeration, is a classical problem in array signal processing which consists in estimating the number of signals received by an array of sensors. In the last decades, numerous approaches to this problem have been proposed. However, the need of working with large-scale arrays (like in massive MIMO systems), low signal-to-noise- ratios, and poor sample regime scenarios, introduce new challenges to order estimation problems. For instance, most of the classical approaches are based on information theoretic criteria, which usually require a large sample size, typically several times larger than the number of sensors. Obtaining a number of samples several times larger than the number of sensors is not always possible with large-scale arrays. In addition, most of the methods found in literature assume that the noise is spatially white, which is very restrictive for many practical scenarios. This dissertation deals with the problem of source enumeration for large-scale arrays, and proposes solutions that work robustly in the small sample regime under various noise models. The first part of the dissertation solves the problem by applying the idea of subspace averaging. The input data are modelled as subspaces, and an average or central subspace is computed. The source enumeration problem can be seen as an estimation of the dimension of the central subspace. A key element of the proposed method is to construct a bootstrap procedure, based on a newly proposed discrete distribution on the manifold of projection matrices, for stochastically generating subspaces from a function of experimentally determined eigenvalues. In this way, the proposed subspace averaging (SA) technique determines the order based on the eigenvalues of an average projection matrix, rather than on the likelihood of a covariance model, penalized by functions of the model order. The proposed SA criterion is especially effective in high-dimensional scenarios with low sample support for uniform linear arrays in the presence of white noise. Further, the proposed SA method is extended for: i) non-white noises, and ii) non-uniform linear arrays. The SA criterion is sensitive with the chosen dimension of extracted subspaces. To solve this problem, we combine the SA technique with a majority vote approach. The number of sources is detected for increasing dimensions of the SA technique and then a majority vote is applied to determine the final estimate. Further, to extend SA for arrays with arbitrary geometries, the SA is combined with a sparse reconstruction (SR) step. In the first step, each received snapshot is approximated by a sparse linear combination of the rest of snapshots. The SR problem is regularized by the logarithm-based surrogate of the l-0 norm and solved using a majorization-minimization approach. Based on the SR solution, a sampling mechanism is proposed in the second step to generate a collection of subspaces, all of which approximately span the same signal subspace. Finally, the dimension of the average of this collection of subspaces provides a robust estimate for the number of sources. The second half of the dissertation introduces a completely different approach to the order estimation for uniform linear arrays, which is based on matrix completion algorithms. This part first discusses the problem of order estimation in the presence of noise whose spatial covariance structure is a diagonal matrix with possibly different variances. The diagonal terms of the sample covariance matrix are removed and, after applying Toeplitz rectification as a denoising step, the signal covariance matrix is reconstructed by using a low-rank matrix completion method adapted to enforce the Toeplitz structure of the sought solution. The proposed source enumeration criterion is based on the Frobenius norm of the reconstructed signal covariance matrix obtained for increasing rank values. The proposed method performs robustly for both small and large-scale arrays with few snapshots. Finally, an approach to work with a reduced number of radio–frequency (RF) chains is proposed. The receiving array relies on antenna switching so that at every time instant only the signals received by a randomly selected subset of antennas are downconverted to baseband and sampled. Low-rank matrix completion (MC) techniques are then used to reconstruct the missing entries of the signal data matrix to keep the angular resolution of the original large-scale array. The proposed MC algorithm exploits not only the low- rank structure of the signal subspace, but also the shift-invariance property of uniform linear arrays, which results in a better estimation of the signal subspace. In addition, the effect of MC on DOA estimation is discussed under the perturbation theory framework. Further, this approach is extended to devise a novel order estimation criterion for missing data scenario. The proposed source enumeration criterion is based on the chordal subspace distance between two sub-matrices extracted from the reconstructed matrix after using MC for increasing rank values. We show that the proposed order estimation criterion performs consistently with a very few available entries in the data matrix.This work was supported by the Ministerio de Ciencia e Innovación (MICINN) of Spain, under grants TEC2016-75067-C4-4-R (CARMEN) and BES-2017-080542

Designer Metasurfaces for On Demand Optical Responses

Nanostructured materials are one of the leading areas in photonics currently. These structures offer almost limitless possibilities in the manipulation of light. Using two different semi-analytical simulation methods, I show a few of the possible properties that these nanostructures possess, including polarization rotation and coupling with electronics

Terahertz (THz) Waveguiding Architecture Featuring Doubly-Corrugated Spoofed Surface Plasmon Polariton (DC-SSPP): Theory and Applications in Micro-Electronics and Sensing

Terahertz (10^12 Hz) has long been considered a missing link between microwave and optical IR spectra. This frequency range has attracted enormous research attentions in recent years, with ever-growing anticipation for its applications in remote sensing, molecular spectroscopy, signal processing and next-generation high-speed electronics. However, its development has been seriously hindered by the lack of waveguiding and manipulating architectures that could support the propagation of THz radiations without excessive signal distortion and power loss. Facing this challenge, this work exploits the spoofed surface plasmon polariton (SSPP) mode of the THz oscillation and introduces the doubly corrugated SSPP (DC-SSPP) architecture to support sub-wavelength, low-dispersion THz transmission. DC-SSPP displays unique bandgap structure, which can be effectively modulated via structural and material variables. These unequaled properties make DC-SSPP the ideal solution to support not only signal transmission but also THz sensing and THz-electronics applications. In this thesis, theoretical analysis is carried out to thoroughly characterize the THz propagation, field distribution and transmission band structures in the novel architecture. Via numerical approximation and finite element simulations, design variations of the DC-SSPP are further studied and optimized to fulfill application-specific requirements. We demonstrate effective DNA sensing by adopting the Mach-Zehnder interferometer (MZI) or waveguide-cavity-waveguide insertions, which showed detectability with minuscule sample size even in the aqueous environment. We manifest high-speed analog-to-digital conversion via a combination of MZI DC-SSPP with nonlinear, partial-coupling detector arrays. Full characterization of the proposed ADC is carried out where high operation speed, small signal distortion, and great output linearity is shown. Also included in this work is a detailed review of the THz emitters and detectors, which are indispensable constituents of the THz system discussed herein. The future of the DC-SSPP in building THz bio-computing and THz digital circuits, considered as the next step of this research work, is also explored and demonstrated with the novel concept of directed logic network.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137130/1/xuzhao_1.pd