Sensor array signal processing : two decades later

Caption title.Includes bibliographical references (p. 55-65).Supported by Army Research Office. DAAL03-92-G-115 Supported by the Air Force Office of Scientific Research. F49620-92-J-2002 Supported by the National Science Foundation. MIP-9015281 Supported by the ONR. N00014-91-J-1967 Supported by the AFOSR. F49620-93-1-0102Hamid Krim, Mats Viberg

On models and methods for direction of arrival estimation of signals in digital wireless communication systems

Orientador: Amauri LopesTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Nesta tese tratamos do problema de estimação de direção de chegada de sinais digitalmente modulados, especificamente encontrado no enlace reverso de sistemas de comunicação semfiomultiusuário em que a estação rádio-base é composta por um arranjo de antenas, posicionadas de acordo com alguma geometria pré-definida. Os objetivos principais da tese são: esclarecer detalhes normalmente não tratados na descrição dos modelos de dados considerados na literatura existente, em especial, a respeito das condições de enlace e do sistema receptor necessárias para suportar a validade dos modelos empregados; agrupar alguns dos métodos de estimação existentes mais relevantes para o contexto tratado e avaliar suas vantagens e desvantagens; e introduzir o método de estimação desenvolvido nas pesquisas do período de doutorado, o qual representa a contribuição maior da tese em termos de metodologia. A solução proposta é fundamentada no critério da máxima verossimilhança e as análises de desempenho feitas através de simulações numéricas mostram que o estimador fornece resultados com qualidade próxima ao limite definido pelo limitante de Cramér-Rao. Algumas características importantes do método são: i) capacidade de operar com qualquer número de fontes se o número de antenas for maior ou igual a dois e o número de snapshots for suficientemente maior que a cardinalidade do espaço de sinal; ii) a qualidade da estimação não é afetada pela separação angular entre as fontes; e iii) possibilidade de operar com arranjos descalibradosAbstract: This thesis deals with the problem of estimating the direction of arrival of digitally modulated signals, specifically found in the uplink of multiuser wireless communication systems where the base station uses an antenna array. The main objectives of the thesis are: to clarify some details normally not treated in the description of the data models assumed in the existing literature, specially, on link and receiver conditions that are necessary to validate the employed models; to present some of the most relevant estimation methods to the current context and to evaluate their advantages and disadvantages; and to introduce the estimation method developed in the researches during the doctoral period, which represents the greater contribution of the thesis in terms of metodology. The proposed solution is fundamented in the maximum likelihood criterion and the performance analysis done through numerical simulations show that the estimator yields results with a quality close to that defined by the Cramér-Rao bound. Some important characteristics of the method are: i) capacity to operate with any number of sources if the number of antennas is greater than or equal to two and the number of snapshots is sufficiently greater than the cardinality of the signal space; ii) the estimation quality does not depend on the angle separation between sources; and iii) possibility to operate with uncalibrated arraysDoutoradoTelecomunicações e TelemáticaDoutor em Engenharia Elétric

수면 호흡음을 이용한 폐쇄성 수면 무호흡 중증도 분류

학위논문 (박사)-- 서울대학교 융합과학기술대학원 융합과학부, 2017. 8. 이교구.Obstructive sleep apnea (OSA) is a common sleep disorder. The symptom has a high prevalence and increases mortality as a risk factor for hypertension and stroke. Sleep disorders occur during sleep, making it difficult for patients to self-perceive themselves, and the actual diagnosis rate is low. Despite the existence of a standard sleep study called a polysomnography (PSG), it is difficult to diagnose the sleep disorders due to complicated test procedures and high medical cost burdens. Therefore, there is an increasing demand for an effective and rational screening test that can determine whether or not to undergo a PSG. In this thesis, we conducted three studies to classify the snoring sounds and OSA severity using only breathing sounds during sleep without additional biosensors. We first identified the classification possibility of snoring sounds related to sleep disorders using the features based on the cyclostationary analysis. Then, we classified the patients OSA severity with the features extracted using temporal and cyclostationary analysis from long-term sleep breathing sounds. Finally, the partial sleep sound extraction, and feature learning process using a convolutional neural network (CNN, or ConvNet) were applied to improve the efficiency and performance of previous snoring sound and OSA severity classification tasks. The sleep breathing sound analysis method using a CNN showed superior classification accuracy of more than 80% (average area under curve > 0.8) in multiclass snoring sounds and OSA severity classification tasks. The proposed analysis and classification method is expected to be used as a screening tool for improving the efficiency of PSG in the future customized healthcare service.Chapter 1. Introduction ................................ .......................1 1.1 Personal healthcare in sleep ................................ ..............1 1.2 Existing approaches and limitations ....................................... 9 1.3 Clinical information related to SRBD ................................ .. ..12 1.4 Study objectives ................................ .........................16 Chapter 2. Overview of Sleep Research using Sleep Breathing Sounds ........... 23 2.1 Previous goals of studies ................................ ................23 2.2 Recording environments and related configurations ........................ 24 2.3 Sleep breathing sound analysis ................................ ...........27 2.4 Sleep breathing sound classification ..................................... 35 2.5 Current limitations ................................ ......................36 Chapter 3. Multiple SRDB-related Snoring Sound Classification .................39 3.1 Introduction ................................ .............................39 3.2 System architecture ................................ ......................41 3.3 Evaluation ................................ ...............................52 3.4 Results ................................ ..................................55 3.5 Discussion ................................ ...............................59 3.6 Summary ................................ ..................................63 Chapter 4. Patients OSA Severity Classification .............................65 4.1 Introduction ................................ .............................65 4.2 Existing Approaches ................................ ......................69 4.3 System Architecture ................................ ......................70 4.4 Evaluation ................................ ...............................85 4.5 Results ................................ ..................................87 4.6 Discussion ................................ ...............................94 4.7 Summary ................................ ..................................97 Chapter 5. Patient OSA Severity Prediction using Deep Learning Techniques .....99 5.1 Introduction ................................ .............................99 5.2 Methods ................................ ..................................101 5.3 Results ................................ ..................................109 5.4 Discussion ................................ ...............................115 5.5 Summary ................................ ..................................118 Chapter 6. Conclusions and Future Work ........................................120 6.1 Conclusions ................................ ..............................120 6.2 Future work ................................ ..............................127Docto

Signal Subspace Processing in the Beam Space of a True Time Delay Beamformer Bank

A number of techniques for Radio Frequency (RF) source location for wide bandwidth signals have been described that utilize coherent signal subspace processing, but often suffer from limitations such as the requirement for preliminary source location estimation, the need to apply the technique iteratively, computational expense or others. This dissertation examines a method that performs subspace processing of the data from a bank of true time delay beamformers. The spatial diversity of the beamformer bank alleviates the need for a preliminary estimate while simultaneously reducing the dimensionality of subsequent signal subspace processing resulting in computational efficiency. The pointing direction of the true time delay beams is independent of frequency, which results in a mapping from element space to beam space that is wide bandwidth in nature. This dissertation reviews previous methods, introduces the present method, presents simulation results that demonstrate the assertions, discusses an analysis of performance in relation to the Cramer-Rao Lower Bound (CRLB) with various levels of noise in the system, and discusses computational efficiency. One limitation of the method is that in practice it may be appropriate for systems that can tolerate a limited field of view. The application of Electronic Intelligence is one such application. This application is discussed as one that is appropriate for a method exhibiting high resolution of very wide bandwidth closely spaced sources and often does not require a wide field of view. In relation to system applications, this dissertation also discusses practical employment of the novel method in terms of antenna elements, arrays, platforms, engagement geometries, and other parameters. The true time delay beam space method is shown through modeling and simulation to be capable of resolving closely spaced very wideband sources over a relevant field of view in a single algorithmic pass, requiring no course preliminary estimation, and exhibiting low computational expense superior to many previous wideband coherent integration techniques

Signal Subspace Processing in the Beam Space of a True Time Delay Beamformer Bank

A number of techniques for Radio Frequency (RF) source location for wide bandwidth signals have been described that utilize coherent signal subspace processing, but often suffer from limitations such as the requirement for preliminary source location estimation, the need to apply the technique iteratively, computational expense or others. This dissertation examines a method that performs subspace processing of the data from a bank of true time delay beamformers. The spatial diversity of the beamformer bank alleviates the need for a preliminary estimate while simultaneously reducing the dimensionality of subsequent signal subspace processing resulting in computational efficiency. The pointing direction of the true time delay beams is independent of frequency, which results in a mapping from element space to beam space that is wide bandwidth in nature. This dissertation reviews previous methods, introduces the present method, presents simulation results that demonstrate the assertions, discusses an analysis of performance in relation to the Cramer-Rao Lower Bound (CRLB) with various levels of noise in the system, and discusses computational efficiency. One limitation of the method is that in practice it may be appropriate for systems that can tolerate a limited field of view. The application of Electronic Intelligence is one such application. This application is discussed as one that is appropriate for a method exhibiting high resolution of very wide bandwidth closely spaced sources and often does not require a wide field of view. In relation to system applications, this dissertation also discusses practical employment of the novel method in terms of antenna elements, arrays, platforms, engagement geometries, and other parameters. The true time delay beam space method is shown through modeling and simulation to be capable of resolving closely spaced very wideband sources over a relevant field of view in a single algorithmic pass, requiring no course preliminary estimation, and exhibiting low computational expense superior to many previous wideband coherent integration techniques

Strategies for Devising Automatic Signal Recognition Algorithms in a Shared Radio Environment

In an increasingly congested and complex radio environment interference is to be expected, which poses problems for Automatic Signal Recognition (ASR) systems. This thesis explores strategies for improving ASR performance in the presence of interference. The thesis breaks the overall research question down into a number of subquestions and explores each of these in turn. A Phase-symmetric Cross Recurrence Plot is developed and used to show how a radio signal can be manipulated to separate information about the modulation from the information being carried. The Logarithmic Cyclic frequency Domain Profile is introduced to illustrate how a logarithmic representation can be used for analysing mixtures of signals with very different cyclic frequencies. After defining a canonical ASR system architecture, the concepts of an Ideal Feature and Interference Selectivity are introduced and applied to typical features used in ASR processing. Finally it is shown how these algorithmic developments can be combined in a Bayesian chain implementation that can accommodate a wide variety of feature extraction algorithms. It is concluded that future ASR systems will require features that can handle a wide range of signal types with much higher levels of interference selectivity if they are to achieve acceptable performance in shared spectrum bands. Intelligent segmentation is shown to be a requirement for future ASR systems unless features can be developed that have near ideal performance

Signal processing techniques for extracting signals with periodic structure : applications to biomedical signals

In this dissertation some advanced methods for extracting sources from single and multichannel data are developed and utilized in biomedical applications. It is assumed that the sources of interest have periodic structure and therefore, the periodicity is exploited in various forms. The proposed methods can even be used for the cases where the signals have hidden periodicities, i.e., the periodic behaviour is not detectable from their time representation or even Fourier transform of the signal. For the case of single channel recordings a method based on singular spectrum anal ysis (SSA) of the signal is proposed. The proposed method is utilized in localizing heart sounds in respiratory signals, which is an essential pre-processing step in most of the heart sound cancellation methods. Artificially mixed and real respiratory signals are used for evaluating the method. It is shown that the performance of the proposed method is superior to those of the other methods in terms of false detection. More over, the execution time is significantly lower than that of the method ranked second in performance. For multichannel data, the problem is tackled using two approaches. First, it is assumed that the sources are periodic and the statistical characteristics of periodic sources are exploited in developing a method to effectively choose the appropriate delays in which the diagonalization takes place. In the second approach it is assumed that the sources of interest are cyclostationary. Necessary and sufficient conditions for extractability of the sources are mathematically proved and the extraction algorithms are proposed. Ballistocardiogram (BCG) artifact is considered as the sum of a number of independent cyclostationary components having the same cycle frequency. The proposed method, called cyclostationary source extraction (CSE), is able to extract these components without much destructive effect on the background electroencephalogram (EEG