Symphony: Localizing Multiple Acoustic Sources with a Single Microphone Array

Sound recognition is an important and popular function of smart devices. The location of sound is basic information associated with the acoustic source. Apart from sound recognition, whether the acoustic sources can be localized largely affects the capability and quality of the smart device's interactive functions. In this work, we study the problem of concurrently localizing multiple acoustic sources with a smart device (e.g., a smart speaker like Amazon Alexa). The existing approaches either can only localize a single source, or require deploying a distributed network of microphone arrays to function. Our proposal called Symphony is the first approach to tackle the above problem with a single microphone array. The insight behind Symphony is that the geometric layout of microphones on the array determines the unique relationship among signals from the same source along the same arriving path, while the source's location determines the DoAs (direction-of-arrival) of signals along different arriving paths. Symphony therefore includes a geometry-based filtering module to distinguish signals from different sources along different paths and a coherence-based module to identify signals from the same source. We implement Symphony with different types of commercial off-the-shelf microphone arrays and evaluate its performance under different settings. The results show that Symphony has a median localization error of 0.694m, which is 68% less than that of the state-of-the-art approach

Estudo de formas de onda e conceção de algoritmos para operação conjunta de sistemas de comunicação e radar

The focus of this thesis is the processing of signals and design of algorithms that can be used to enable radar functions in communications systems. Orthogonal frequency division multiplexing (OFDM) is a popular multicarrier modulation waveform in communication systems. As a wideband signal, OFDM improves resolution and enables spectral efficiency in radar systems, while also improving detection performance thanks to its inherent frequency diversity. This thesis aims to use multicarrier waveforms for radar systems, to enable the simultaneous operation of radar and communication functions on the same device. The thesis is divided in two parts. The first part, studies the adaptation and application of other multicarrier waveforms to radar functions. At the present time many studies have been carried out to jointly use the OFDM signal for communication and radar functions, but other waveforms have shown to be possible candidates for communication applications. Therefore, studies on the evaluation of the application of these same signals to radar functions are necessary. In this thesis, to demonstrate that other multicarrier waveforms can overcome the OFDM waveform in radar/communication (RadCom) systems, we propose the adaptation of the filter bank multicarrier (FBMC), generalized frequency division multiplexing (GFDM) and universal filtering multicarrier (UFMC) waveforms for radar functions. These alternative waveforms were compared performance-wise regarding achievable target parameter estimation performance, amount of residual background noise in the radar image, impact of intersystem interference and flexibility of parameterization. In the second part of the thesis, signal processing techniques are explored to solve some of the limitations of the use of multicarrier waveforms for RadCom systems. Radar systems based on OFDM are promising candidates for future intelligent transport networks. Exploring the dual functionality enabled by OFDM, we presents cooperative methods for high-resolution delay-Doppler and direction-of-arrival estimation. High-resolution parameter estimation is an important requirement for automotive radar systems, especially in multi-target scenarios that require reliable target separation performance. By exploring the cooperation between vehicles, the studies presented in this thesis also enable the distributed tracking of targets. The result is a highly accurate multi-target tracking across the entire cooperative vehicle network, leading to improvements in transport reliability and safety.O foco desta tese é o processamento de sinais e desenvolvimento de algoritmos que podem ser utilizados para a habilitar a função de radar nos sistemas de comunicação. OFDM (Orthogonal Frequency Division Multiplexing) é uma forma de onda com modulação multi-portadora, popular em sistemas de comunicação. Para sistemas de radar, O OFDM melhora a resolução e fornece eficiência espectral, além disso sua diversidade de frequências melhora o desempenho na detecção do radar. Essa tese tem como objetivo utilizar formas de onda multi-portadoras para sistemas de radar, possibilitando a operação simultânea de funções de radar e de comunicação num mesmo dispositivo. A tese esta dividida em duas partes. Na primeira parte da tese são realizados estudos da adaptabilidade de outras formas de onda multi-portadora para funções de radar. Nos dias atuais, muitos estudos sobre o uso do sinal OFDM para funções de comunicação e radar vêm sendo realizados, no entanto, outras formas de onda mostram-se possíveis candidatas a aplicações em sistemas de comunicação, e assim, avaliações para funções de sistema de radar se tornam necessárias. Nesta tese, com a intenção de demonstrar que formas de onda multi-portadoras alternativas podem superar o OFDM nos sistemas de Radar/comunicação (RadCom), propomos a adaptação das seguintes formas de onda: FBMC (Filter Bank Multicarrier); GFDM (Generalized Frequency Division Multiplexing); e UFMC (Universal Filtering Multicarrier) para funções de radar. Também produzimos uma análise de desempenho dessas formas de onda sobre o aspecto da estimativa de parâmetros-alvo, ruído de fundo, interferência entre sistemas e parametrização do sistema. Na segunda parte da tese serão explorados técnicas de processamento de sinal de forma a solucionar algumas das limitações do uso de formas de ondas multi-portadora para sistemas RadCom. Os sistemas de radar baseados no OFDM são candidatos promissores para futuras redes de transporte inteligentes, porque combinam funções de estimativa de alvo com funções de rede de comunicação em um único sistema. Explorando a funcionalidade dupla habilitada pelo OFDM, nesta tese, apresentamos métodos cooperativos de alta resolução para estimar o posição, velocidade e direção dos alvos. A estimativa de parâmetros de alta resolução é um requisito importante para sistemas de radar automotivo, especialmente em cenários de múltiplos alvos que exigem melhor desempenho de separação de alvos. Ao explorar a cooperação entre veículos, os estudos apresentados nesta tese também permitem o rastreamento distribuído de alvos. O resultado é um rastreamento multi-alvo altamente preciso em toda a rede de veículos cooperativos, levando a melhorias na confiabilidade e segurança do transporte.Programa Doutoral em Telecomunicaçõe

Suppression of thermal conductivity without impeding electron mobility in n-type XNiSn half-Heusler thermoelectrics

We outline a strategy to improve the thermoelectric performance of n-type XNiSn based half-Heusler alloys through Cu doping into vacant tetrahedral sites. A comprehensive combination of structural characterisation and modelling is employed to discriminate the competing mechanisms for thermoelectric enhancement. During synthesis a mineralising effect occurs that improves the homogeneity of the alloying elements Ti, Zr and Hf, and promotes grain growth, leading to a doubling of the electron mobility. In the formed materials, Cu is a strong n-type dopant, like Sb, but occupies the interstitial site and strongly enhances phonon scattering without diminishing carrier mobility (in contrast to interstitial Ni). Simultaneous alloying with Ti, Zr and Hf serves to minimise the thermal conductivity via regular mass disorder and strain effects. A best electronic power factor, S2/ρ, of 3.6 mW m−1 K−2 and maximum ZT of 0.8 at 773 K were observed for a Ti0.5Zr0.25Hf0.25NiCu0.025Sn composition, enabling promising device power densities of ∼6 W cm−2 and ∼8% conversion efficiency from a 450 K gradient. These findings are important because they provide new insight into the mechanisms underpinning high ZT in the XNiSn system and indicate a direction for further improvements in thermoelectric performance

Human presence detection using millimeter-wave radiometry

textA novel method of human presence detection using passive millimeter-wave sensors is presented. The method focuses on detecting a standing human from a moving platform in a cluttered outdoor environment using millimeter-wave radiometry, which has not been attempted before. Ka-band radiometers are used in total power mode as well as correlation mode, which ideally responds well to self-luminous objects such as humans. The intrinsic radiative power from a human is derived as well as the responses of the total power and correlation mode. The application of correlation radiometer theory to the detection of self-luminous objects at close range is presented in the context of human presence detection. Modifications and additions to techniques developed in radio astronomy and remote sensing for close range terrestrial situations are developed and discussed. The correlation radiometer fringe frequency is analyzed in the context of the scanning beam detection system and is estimated using MUSIC and ESPRIT. Detection and classification of humans is accomplished using a Naïve Bayesian classifier. The performance of the classifier is measured using the F1-measure and the receiver operating characteristic.Electrical and Computer Engineerin

Joint Communication and Positioning based on Channel Estimation

Mobile wireless communication systems have rapidly and globally become an integral part of everyday life and have brought forth the internet of things. With the evolution of mobile wireless communication systems, joint communication and positioning becomes increasingly important and enables a growing range of new applications. Humanity has already grown used to having access to multimedia data everywhere at every time and thereby employing all sorts of location-based services. Global navigation satellite systems can provide highly accurate positioning results whenever a line-of-sight path is available. Unfortunately, harsh physical environments are known to degrade the performance of existing systems. Therefore, ground-based systems can assist the existing position estimation gained by satellite systems. Determining positioning-relevant information from a unified signal structure designed for a ground-based joint communication and positioning system can either complement existing systems or substitute them. Such a system framework promises to enhance the existing systems by enabling a highly accurate and reliable positioning performance and increased coverage. Furthermore, the unified signal structure yields synergetic effects. In this thesis, I propose a channel estimation-based joint communication and positioning system that employs a virtual training matrix. This matrix consists of a relatively small training percentage, plus the detected communication data itself. Via a core semi- blind estimation approach, this iteratively includes the already detected data to accurately determine the positioning-relevant parameter, by mutually exchanging information between the communication part and the positioning part of the receiver. Synergy is created. I propose a generalized system framework, suitable to be used in conjunction with various communication system techniques. The most critical positioning-relevant parameter, the time-of-arrival, is part of a physical multipath parameter vector. Estimating the time-of-arrival, therefore, means solving a global, non-linear, multi-dimensional optimization problem. More precisely, it means solving the so-called inverse problem. I thoroughly assess various problem formulations and variations thereof, including several different measurements and estimation algorithms. A significant challenge, when it comes to solving the inverse problem to determine the positioning-relevant path parameters, is imposed by realistic multipath channels. Most parameter estimation algorithms have proven to perform well in moderate multipath environments. It is mathematically straightforward to optimize this performance in the sense that the number of observations has to exceed the number of parameters to be estimated. The typical parameter estimation problem, on the other hand, is based on channel estimates, and it assumes that so-called snapshot measurements are available. In the case of realistic channel models, however, the number of observations does not necessarily exceed the number of unknowns. In this thesis, I overcome this problem, proposing a method to reduce the problem dimensionality via joint model order selection and parameter estimation. Employing the approximated and estimated parameter covariance matrix inherently constrains the estimation problem’s model order selection to result in optimal parameter estimation performance and hence optimal positioning performance. To compare these results with the optimally achievable solution, I introduce a focused order-related lower bound in this thesis. Additionally, I use soft information as a weighting matrix to enhance the positioning algorithm positioning performance. For demonstrating the feasibility and the interplay of the proposed system components, I utilize a prototype system, based on multi-layer interleave division multiple access. This proposed system framework and the investigated techniques can be employed for multiple existing systems or build the basis for future joint communication and positioning systems. The assessed estimation algorithms are transferrable to all kinds of joint communication and positioning system designs. This thesis demonstrates their capability to, in principle, successfully cope with challenging estimation problems stemming from harsh physical environments

Efficient algorithms and data structures for compressive sensing

Wegen der kontinuierlich anwachsenden Anzahl von Sensoren, und den stetig wachsenden Datenmengen, die jene produzieren, stößt die konventielle Art Signale zu verarbeiten, beruhend auf dem Nyquist-Kriterium, auf immer mehr Hindernisse und Probleme. Die kürzlich entwickelte Theorie des Compressive Sensing (CS) formuliert das Versprechen einige dieser Hindernisse zu beseitigen, indem hier allgemeinere Signalaufnahme und -rekonstruktionsverfahren zum Einsatz kommen können. Dies erlaubt, dass hierbei einzelne Abtastwerte komplexer strukturierte Informationen über das Signal enthalten können als dies bei konventiellem Nyquistsampling der Fall ist. Gleichzeitig verändert sich die Signalrekonstruktion notwendigerweise zu einem nicht-linearen Vorgang und ebenso müssen viele Hardwarekonzepte für praktische Anwendungen neu überdacht werden. Das heißt, dass man zwischen der Menge an Information, die man über Signale gewinnen kann, und dem Aufwand für das Design und Betreiben eines Signalverarbeitungssystems abwägen kann und muss. Die hier vorgestellte Arbeit trägt dazu bei, dass bei diesem Abwägen CS mehr begünstigt werden kann, indem neue Resultate vorgestellt werden, die es erlauben, dass CS einfacher in der Praxis Anwendung finden kann, wobei die zu erwartende Leistungsfähigkeit des Systems theoretisch fundiert ist. Beispielsweise spielt das Konzept der Sparsity eine zentrale Rolle, weshalb diese Arbeit eine Methode präsentiert, womit der Grad der Sparsity eines Vektors mittels einer einzelnen Beobachtung geschätzt werden kann. Wir zeigen auf, dass dieser Ansatz für Sparsity Order Estimation zu einem niedrigeren Rekonstruktionsfehler führt, wenn man diesen mit einer Rekonstruktion vergleicht, welcher die Sparsity des Vektors unbekannt ist. Um die Modellierung von Signalen und deren Rekonstruktion effizienter zu gestalten, stellen wir das Konzept von der matrixfreien Darstellung linearer Operatoren vor. Für die einfachere Anwendung dieser Darstellung präsentieren wir eine freie Softwarearchitektur und demonstrieren deren Vorzüge, wenn sie für die Rekonstruktion in einem CS-System genutzt wird. Konkret wird der Nutzen dieser Bibliothek, einerseits für das Ermitteln von Defektpositionen in Prüfkörpern mittels Ultraschall, und andererseits für das Schätzen von Streuern in einem Funkkanal aus Ultrabreitbanddaten, demonstriert. Darüber hinaus stellen wir für die Verarbeitung der Ultraschalldaten eine Rekonstruktionspipeline vor, welche Daten verarbeitet, die im Frequenzbereich Unterabtastung erfahren haben. Wir beschreiben effiziente Algorithmen, die bei der Modellierung und der Rekonstruktion zum Einsatz kommen und wir leiten asymptotische Resultate für die benötigte Anzahl von Messwerten, sowie die zu erwartenden Lokalisierungsgenauigkeiten der Defekte her. Wir zeigen auf, dass das vorgestellte System starke Kompression zulässt, ohne die Bildgebung und Defektlokalisierung maßgeblich zu beeinträchtigen. Für die Lokalisierung von Streuern mittels Ultrabreitbandradaren stellen wir ein CS-System vor, welches auf einem Random Demodulators basiert. Im Vergleich zu existierenden Messverfahren ist die hieraus resultierende Schätzung der Kanalimpulsantwort robuster gegen die Effekte von zeitvarianten Funkkanälen. Um den inhärenten Modellfehler, den gitterbasiertes CS begehen muss, zu beseitigen, zeigen wir auf wie Atomic Norm Minimierung es erlaubt ohne die Einschränkung auf ein endliches und diskretes Gitter R-dimensionale spektrale Komponenten aus komprimierten Beobachtungen zu schätzen. Hierzu leiten wir eine R-dimensionale Variante des ADMM her, welcher dazu in der Lage ist die Signalkovarianz in diesem allgemeinen Szenario zu schätzen. Weiterhin zeigen wir, wie dieser Ansatz zur Richtungsschätzung mit realistischen Antennenarraygeometrien genutzt werden kann. In diesem Zusammenhang präsentieren wir auch eine Methode, welche mittels Stochastic gradient descent Messmatrizen ermitteln kann, die sich gut für Parameterschätzung eignen. Die hieraus resultierenden Kompressionsverfahren haben die Eigenschaft, dass die Schätzgenauigkeit über den gesamten Parameterraum ein möglichst uniformes Verhalten zeigt. Zuletzt zeigen wir auf, dass die Kombination des ADMM und des Stochastic Gradient descent das Design eines CS-Systems ermöglicht, welches in diesem gitterfreien Szenario wünschenswerte Eigenschaften hat.Along with the ever increasing number of sensors, which are also generating rapidly growing amounts of data, the traditional paradigm of sampling adhering the Nyquist criterion is facing an equally increasing number of obstacles. The rather recent theory of Compressive Sensing (CS) promises to alleviate some of these drawbacks by proposing to generalize the sampling and reconstruction schemes such that the acquired samples can contain more complex information about the signal than Nyquist samples. The proposed measurement process is more complex and the reconstruction algorithms necessarily need to be nonlinear. Additionally, the hardware design process needs to be revisited as well in order to account for this new acquisition scheme. Hence, one can identify a trade-off between information that is contained in individual samples of a signal and effort during development and operation of the sensing system. This thesis addresses the necessary steps to shift the mentioned trade-off more to the favor of CS. We do so by providing new results that make CS easier to deploy in practice while also maintaining the performance indicated by theoretical results. The sparsity order of a signal plays a central role in any CS system. Hence, we present a method to estimate this crucial quantity prior to recovery from a single snapshot. As we show, this proposed Sparsity Order Estimation method allows to improve the reconstruction error compared to an unguided reconstruction. During the development of the theory we notice that the matrix-free view on the involved linear mappings offers a lot of possibilities to render the reconstruction and modeling stage much more efficient. Hence, we present an open source software architecture to construct these matrix-free representations and showcase its ease of use and performance when used for sparse recovery to detect defects from ultrasound data as well as estimating scatterers in a radio channel using ultra-wideband impulse responses. For the former of these two applications, we present a complete reconstruction pipeline when the ultrasound data is compressed by means of sub-sampling in the frequency domain. Here, we present the algorithms for the forward model, the reconstruction stage and we give asymptotic bounds for the number of measurements and the expected reconstruction error. We show that our proposed system allows significant compression levels without substantially deteriorating the imaging quality. For the second application, we develop a sampling scheme to acquire the channel Impulse Response (IR) based on a Random Demodulator that allows to capture enough information in the recorded samples to reliably estimate the IR when exploiting sparsity. Compared to the state of the art, this in turn allows to improve the robustness to the effects of time-variant radar channels while also outperforming state of the art methods based on Nyquist sampling in terms of reconstruction error. In order to circumvent the inherent model mismatch of early grid-based compressive sensing theory, we make use of the Atomic Norm Minimization framework and show how it can be used for the estimation of the signal covariance with R-dimensional parameters from multiple compressive snapshots. To this end, we derive a variant of the ADMM that can estimate this covariance in a very general setting and we show how to use this for direction finding with realistic antenna geometries. In this context we also present a method based on a Stochastic gradient descent iteration scheme to find compression schemes that are well suited for parameter estimation, since the resulting sub-sampling has a uniform effect on the whole parameter space. Finally, we show numerically that the combination of these two approaches yields a well performing grid-free CS pipeline

Eigenvector-based multidimensional frequency estimation : identifiability, performance, and applications.

Multidimensional frequency estimation is a classic signal processing problem that has versatile applications in sensor array processing and wireless communications. Eigenvalue-based two-dimensional (2-D) and N -dimensional ( N -D) frequency estimation algorithms have been well documented, however, these algorithms suffer from limited identifiability and demanding computations. This dissertation develops a framework on eigenvector-based N -D frequency estimation, which contains several novel algorithms that estimate a structural matrix from eigenvectors and then resolve the N -D frequencies by dividing the elements of the structural matrix. Compared to the existing eigenvalue-based algorithms, these eigenvector-based algorithms can achieve automatic pairing without an extra frequency pairing step, and tins the computational complexity is reduced. The identifiability, performance, and complexity of these algorithms are also systematically studied. Based on this study, the most relaxed identifiability condition for the N -D frequency estimation problem is given and an effective approach using optimized weighting factors to improve the performance of frequency estimation is developed. These results are applied in wireless communication for time-varying multipath channel estimation and prediction, as well as for joint 2-D Direction-of-arrival (DOA) tracking of multiple moving targets