174 research outputs found
Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis
The widespread use of multi-sensor technology and the emergence of big
datasets has highlighted the limitations of standard flat-view matrix models
and the necessity to move towards more versatile data analysis tools. We show
that higher-order tensors (i.e., multiway arrays) enable such a fundamental
paradigm shift towards models that are essentially polynomial and whose
uniqueness, unlike the matrix methods, is guaranteed under verymild and natural
conditions. Benefiting fromthe power ofmultilinear algebra as theirmathematical
backbone, data analysis techniques using tensor decompositions are shown to
have great flexibility in the choice of constraints that match data properties,
and to find more general latent components in the data than matrix-based
methods. A comprehensive introduction to tensor decompositions is provided from
a signal processing perspective, starting from the algebraic foundations, via
basic Canonical Polyadic and Tucker models, through to advanced cause-effect
and multi-view data analysis schemes. We show that tensor decompositions enable
natural generalizations of some commonly used signal processing paradigms, such
as canonical correlation and subspace techniques, signal separation, linear
regression, feature extraction and classification. We also cover computational
aspects, and point out how ideas from compressed sensing and scientific
computing may be used for addressing the otherwise unmanageable storage and
manipulation problems associated with big datasets. The concepts are supported
by illustrative real world case studies illuminating the benefits of the tensor
framework, as efficient and promising tools for modern signal processing, data
analysis and machine learning applications; these benefits also extend to
vector/matrix data through tensorization. Keywords: ICA, NMF, CPD, Tucker
decomposition, HOSVD, tensor networks, Tensor Train
Advanced Algebraic Concepts for Efficient Multi-Channel Signal Processing
Unsere moderne Gesellschaft ist Zeuge eines fundamentalen Wandels in der Art und Weise
wie wir mit Technologie interagieren. Geräte werden zunehmend intelligenter - sie verfügen
über mehr und mehr Rechenleistung und häufiger über eigene Kommunikationsschnittstellen.
Das beginnt bei einfachen Haushaltsgeräten und reicht über Transportmittel bis zu großen
überregionalen Systemen wie etwa dem Stromnetz. Die Erfassung, die Verarbeitung und der
Austausch digitaler Informationen gewinnt daher immer mehr an Bedeutung. Die Tatsache,
dass ein wachsender Anteil der Geräte heutzutage mobil und deshalb batteriebetrieben ist,
begründet den Anspruch, digitale Signalverarbeitungsalgorithmen besonders effizient zu gestalten.
Dies kommt auch dem Wunsch nach einer Echtzeitverarbeitung der großen anfallenden
Datenmengen zugute.
Die vorliegende Arbeit demonstriert Methoden zum Finden effizienter algebraischer Lösungen
für eine Vielzahl von Anwendungen mehrkanaliger digitaler Signalverarbeitung. Solche Ansätze
liefern nicht immer unbedingt die bestmögliche Lösung, kommen dieser jedoch häufig recht
nahe und sind gleichzeitig bedeutend einfacher zu beschreiben und umzusetzen. Die einfache
Beschreibungsform ermöglicht eine tiefgehende Analyse ihrer Leistungsfähigkeit, was für den
Entwurf eines robusten und zuverlässigen Systems unabdingbar ist. Die Tatsache, dass sie nur
gebräuchliche algebraische Hilfsmittel benötigen, erlaubt ihre direkte und zügige Umsetzung
und den Test unter realen Bedingungen.
Diese Grundidee wird anhand von drei verschiedenen Anwendungsgebieten demonstriert.
Zunächst wird ein semi-algebraisches Framework zur Berechnung der kanonisch polyadischen
(CP) Zerlegung mehrdimensionaler Signale vorgestellt. Dabei handelt es sich um ein sehr
grundlegendes Werkzeug der multilinearen Algebra mit einem breiten Anwendungsspektrum
von Mobilkommunikation über Chemie bis zur Bildverarbeitung. Verglichen mit existierenden
iterativen Lösungsverfahren bietet das neue Framework die Möglichkeit, den Rechenaufwand
und damit die Güte der erzielten Lösung zu steuern. Es ist außerdem weniger anfällig gegen eine
schlechte Konditionierung der Ausgangsdaten. Das zweite Gebiet, das in der Arbeit besprochen
wird, ist die unterraumbasierte hochauflösende Parameterschätzung für mehrdimensionale Signale,
mit Anwendungsgebieten im RADAR, der Modellierung von Wellenausbreitung, oder
bildgebenden Verfahren in der Medizin. Es wird gezeigt, dass sich derartige mehrdimensionale
Signale mit Tensoren darstellen lassen. Dies erlaubt eine natürlichere Beschreibung und eine
bessere Ausnutzung ihrer Struktur als das mit Matrizen möglich ist. Basierend auf dieser Idee
entwickeln wir eine tensor-basierte Schätzung des Signalraums, welche genutzt werden kann
um beliebige existierende Matrix-basierte Verfahren zu verbessern. Dies wird im Anschluss
exemplarisch am Beispiel der ESPRIT-artigen Verfahren gezeigt, für die verbesserte Versionen
vorgeschlagen werden, die die mehrdimensionale Struktur der Daten (Tensor-ESPRIT),
nichzirkuläre Quellsymbole (NC ESPRIT), sowie beides gleichzeitig (NC Tensor-ESPRIT) ausnutzen.
Um die endgültige Schätzgenauigkeit objektiv einschätzen zu können wird dann ein
Framework für die analytische Beschreibung der Leistungsfähigkeit beliebiger ESPRIT-artiger
Algorithmen diskutiert. Verglichen mit existierenden analytischen Ausdrücken ist unser Ansatz
allgemeiner, da keine Annahmen über die statistische Verteilung von Nutzsignal und
Rauschen benötigt werden und die Anzahl der zur Verfügung stehenden Schnappschüsse beliebig
klein sein kann. Dies führt auf vereinfachte Ausdrücke für den mittleren quadratischen
Schätzfehler, die Schlussfolgerungen über die Effizienz der Verfahren unter verschiedenen Bedingungen
zulassen. Das dritte Anwendungsgebiet ist der bidirektionale Datenaustausch mit
Hilfe von Relay-Stationen. Insbesondere liegt hier der Fokus auf Zwei-Wege-Relaying mit Hilfe
von Amplify-and-Forward-Relays mit mehreren Antennen, da dieser Ansatz ein besonders gutes
Kosten-Nutzen-Verhältnis verspricht. Es wird gezeigt, dass sich die nötige Kanalkenntnis
mit einem einfachen algebraischen Tensor-basierten Schätzverfahren gewinnen lässt. Außerdem
werden Verfahren zum Finden einer günstigen Relay-Verstärkungs-Strategie diskutiert. Bestehende
Ansätze basieren entweder auf komplexen numerischen Optimierungsverfahren oder auf
Ad-Hoc-Ansätzen die keine zufriedenstellende Bitfehlerrate oder Summenrate liefern. Deshalb
schlagen wir algebraische Ansätze zum Finden der Relayverstärkungsmatrix vor, die von relevanten
Systemmetriken inspiriert sind und doch einfach zu berechnen sind. Wir zeigen das
algebraische ANOMAX-Verfahren zum Erreichen einer niedrigen Bitfehlerrate und seine Modifikation
RR-ANOMAX zum Erreichen einer hohen Summenrate. Für den Spezialfall, in dem
die Endgeräte nur eine Antenne verwenden, leiten wir eine semi-algebraische Lösung zum
Finden der Summenraten-optimalen Strategie (RAGES) her. Anhand von numerischen Simulationen
wird die Leistungsfähigkeit dieser Verfahren bezüglich Bitfehlerrate und erreichbarer
Datenrate bewertet und ihre Effektivität gezeigt.Modern society is undergoing a fundamental change in the way we interact with technology.
More and more devices are becoming "smart" by gaining advanced computation capabilities
and communication interfaces, from household appliances over transportation systems to large-scale
networks like the power grid. Recording, processing, and exchanging digital information
is thus becoming increasingly important. As a growing share of devices is nowadays mobile
and hence battery-powered, a particular interest in efficient digital signal processing techniques
emerges.
This thesis contributes to this goal by demonstrating methods for finding efficient algebraic
solutions to various applications of multi-channel digital signal processing. These may not
always result in the best possible system performance. However, they often come close while
being significantly simpler to describe and to implement. The simpler description facilitates a
thorough analysis of their performance which is crucial to design robust and reliable systems.
The fact that they rely on standard algebraic methods only allows their rapid implementation
and test under real-world conditions.
We demonstrate this concept in three different application areas. First, we present a semi-algebraic
framework to compute the Canonical Polyadic (CP) decompositions of multidimensional
signals, a very fundamental tool in multilinear algebra with applications ranging from
chemistry over communications to image compression. Compared to state-of-the art iterative
solutions, our framework offers a flexible control of the complexity-accuracy trade-off and
is less sensitive to badly conditioned data. The second application area is multidimensional
subspace-based high-resolution parameter estimation with applications in RADAR, wave propagation
modeling, or biomedical imaging. We demonstrate that multidimensional signals can
be represented by tensors, providing a convenient description and allowing to exploit the
multidimensional structure in a better way than using matrices only. Based on this idea,
we introduce the tensor-based subspace estimate which can be applied to enhance existing
matrix-based parameter estimation schemes significantly. We demonstrate the enhancements
by choosing the family of ESPRIT-type algorithms as an example and introducing enhanced
versions that exploit the multidimensional structure (Tensor-ESPRIT), non-circular source
amplitudes (NC ESPRIT), and both jointly (NC Tensor-ESPRIT). To objectively judge the
resulting estimation accuracy, we derive a framework for the analytical performance assessment
of arbitrary ESPRIT-type algorithms by virtue of an asymptotical first order perturbation
expansion. Our results are more general than existing analytical results since we do not need
any assumptions about the distribution of the desired signal and the noise and we do not
require the number of samples to be large. At the end, we obtain simplified expressions for the
mean square estimation error that provide insights into efficiency of the methods under various
conditions. The third application area is bidirectional relay-assisted communications. Due to
its particularly low complexity and its efficient use of the radio resources we choose two-way
relaying with a MIMO amplify and forward relay. We demonstrate that the required channel
knowledge can be obtained by a simple algebraic tensor-based channel estimation scheme. We
also discuss the design of the relay amplification matrix in such a setting. Existing approaches
are either based on complicated numerical optimization procedures or on ad-hoc solutions
that to not perform well in terms of the bit error rate or the sum-rate. Therefore, we propose
algebraic solutions that are inspired by these performance metrics and therefore perform well
while being easy to compute. For the MIMO case, we introduce the algebraic norm maximizing
(ANOMAX) scheme, which achieves a very low bit error rate, and its extension Rank-Restored
ANOMAX (RR-ANOMAX) that achieves a sum-rate close to an upper bound. Moreover, for
the special case of single antenna terminals we derive the semi-algebraic RAGES scheme which
finds the sum-rate optimal relay amplification matrix based on generalized eigenvectors. Numerical
simulations evaluate the resulting system performance in terms of bit error rate and
system sum rate which demonstrates the effectiveness of the proposed algebraic solutions
Scaling transform based information geometry method for DOA estimation
By exploiting the relationship between probability density and the differential geometry structure of received data and geodesic distance, the recently proposed information geometry (IG) method can provide higher accuracy and resolution ability for direction of arrival (DOA) estimation than many existing methods. However, its performance is not robust even for high signal to noise ratio (SNR). To have a deep understanding of its unstable performance, a theoretical analysis of the IG method is presented by deriving the relationship between the cost function and the number of array elements, powers and DOAs of source signals, and noise power. Then, to make better use of the nonlinear and super resolution property of the cost function, a Scaling TRansform based INformation Geometry (STRING) method is proposed, which simply scales the array received data or its covariance matrix by a real number. However, the expression for the optimum value of the scalar is complicated and related to the unknown signal DOAs and powers. Hence, a decision criterion and a simple search based procedure are developed, guaranteeing a robust performance. As demonstrated by computer simulations, the proposed STRING method has the best and robust angle resolution performance compared with many existing high resolution methods and even outperforms the classic Cramer-Rao bound (CRB), although at the cost of a bias in the estimation results
Time-delay estimation under non-clustered and clustered scenarios for GNSS signals
Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2021.Aplicações que empregam sistemas globais de navegação por satélite, do inglês Global
Navigation Satellite Systems (GNSS) para prover posicionamento acurado estão sujeitos a
degradação drástica não só por intereferências eletromagnéticas, como também componentes
de multipercurso causados por reflexões e refrações no ambiente. Aplicações de segurança
crítica como veículos autonômos e aviação civil, e aplicações de risco crítico como gestão de
pesca, pedágio automático, e agricultura de precisão dependem de posicionamento acurado
sob cenários complicados. Tipicamente quanto mais agrupamento ocorre entre o componente de linha de visada, do inglês line-of-sight (LOS) e componentes de multipercurso ou
não-linha de visada, do inglês non-line-of-sight (NLOS), menos acurada é a estimação da
posição. Abordagens tensorials estado da arte para receptores GNSS baseado em arranjos
de antenas utilizam processamento tensorial de sinais para separar o componente LOS dos
componentes NLOS, assim mitigando os efeitos destes, utilizando decomposição em valores singulares multilinear, do inglês multilinear singular value decomposition (MLSVD)
para gerar um autofiltro de order superior, do inglês higher-order eigenfilter (HOE) com
pré-processamento por média frente-costas, do inglês forward-backward averaging (FBA),
e suavização espacial expandida, do inglês expanded spatial smoothing (ESPS), estimação
de direção de chegada, do inglês direction of arrival (DoA) e fatorização Khatri-Rao, do
inglês Khatri-Rao factorization (KRF), estimação de Procrustes e fatorização Khatri-Rao
(ProKRaft), e o sistema semi-algébrico de decomposição poliádica canônica por diagonalização matricial simultânea, do inglês semi-algebraic framework for approximate canonical
polyadic decomposition via simultaneous matrix diagonalization (SECSI), respectivamente.
Propomos duas abordagens de processamento para estimação de atraso, do inglês time-delay
estimation (TDE). A primeira é a abordagem em lotes utilizando dados de vários períodos
do sinal. Usando estimação em lotes propomos duas abordagens algébricas para TDE, em
que diagonalizaçao é efetivada por decomposição generalizada em autovalores, do inglês
generalized eigenvalue decomposition (GEVD), das primeiras duas fatias frontais do tensor núcleo do tensor de dados, estimado por MLSVD. Esta primeira abordagem, como os
métodos citados, na quais simulações foram feitas com 1 componente LOS e 1 componente
NLOS, assim os dados observados tem posto cheio em todos seus modos, não faz suposições
sobre o posto do tensor de dados. A segunda abordagem supõe cenários nos quais mais de
1 componente NLOS está presente e são agregados (clustered em inglês), assim vários vetores de uma das matrizes-fator que formam o tensor de dados são altamente correlacionaiii
dos, resultando num tensor de dados que é de posto deficiente em pelo menos um modo.
Os esquemas algébricos baseados em tensores propostos utilizam a decomposição poliádica
canônica por decomposição generalizada em autovalores, do inglês canonical polyadic decomposition via generalized eigenvalue decomposition (CPD-GEVD), e a decomposição em
termos de posto-(Lr, Lr, 1) por decomposição generalizada em autovalores, do inglês decomposition in multilinear rank-(Lr, Lr, 1) terms via generalized eigenvalue decomposition
((Lr, Lr, 1)-GEVD) para melhorar a TDE do componente LOS sob cenários desafiadores. A
segunda é a abordagem de processamento adaptativo de amostras individuais utilizando rastreamento de subespaço a cada período de código, epoch em inglês. Usando processamento
adaptativo propomos duas abordagem, uma aplicando FBA expandido (EFBA) e ESPS ao
dados e estimando um HOE, e outra usando usa estimação paramétrica para estimar a DoA.
Estendendo o modelo para um arranjo retangular uniforme, do inglês uniform rectangular
array (URA), o fluxo de dados são tensores de terceira ordem. Para este modelo propomos
três abordagens para TDE baseado em HOE, CPD-GEVD, e ESPRIT tensorial, respectivamente e empregando uma estratégia de truncamento sequencial para reduzir a quantidade de
operações necessárias para cada modo do tensorCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).Applications employing Global Navigation Satellite Systems (GNSS) to provide accurate positioning are subject to drastic degradation not only due to electromagnetic interference, but also due to multipath components caused by reflections and refractions in the
environment. Safety-critical applications such as autonomous vehicles and civil aviation,
and liability-critical applications such as fisheries management, automatic tolling, and precision agriculture depend on accurate positioning under such demanding scenarios. Typically,
the more clustering occurs between the line-of-sight (LOS) component and multipath or
non-line-of-sight (NLOS) components, the more inaccurate is the estimation of the positioning. State-of-the-art tensor based approaches for antenna array-based GNSS receivers apply
tensor-based signal processing to separate the LOS components from NLOS components,
thus mitigating the effects of the latter, using the multilinear singular value decomposition
(MLSVD) to generate a higher-order eigenfilter (HOE) with forward-backward averaging
(FBA) and expanded spatial smoothing (ESPS) preprocessing, direction of arrival (DoA) estimation and Khatri-Rao factorization (KRF), Procrustes estimation and Khatri-Rao factorization (ProKRaft), and the semi-algebraic framework for approximate canonical polyadic
decomposition via simultaneous matrix diagonalization (SECSI), respectively. These approaches use filtering, parameter estimation and filtering, iterative algebraic factor matrix
estimation and filtering, and algebraic factor matrix estimation, respectively. We propose
two processing approaches to time-delay estimation (TDE). The first is batch processing
taking data from several signal periods. Using batch processing we propose two algebraic
approaches to TDE, in which diagonalization is achieved using the generalized eigenvalue
decomposition (GEVD) of the first two frontal slices of the measurement tensor’s core tensor,
estimated via MLSVD. The former approach, like the cited methods, in which simulations
were performed with 1 LOS component and 1 NLOS component, and thus the measured data
has full-rank tensor in all its modes, makes no assumption about the rank of the measurement tensor. The latter approach assumes scenarios in which more than 1 NLOS component
is present and these are clustered, thus several vectors of one of the factor matrices which
forms the tensor data are highly correlated, resulting in a rank-deficient measurement tensor
in at least one mode. These proposed algebraic tensor-based schemes utilize the canonical
polyadic decomposition via generalized eigenvalue decomposition (CPD-GEVD) and the decomposition in multilinear rank-(Lr, Lr, 1) terms via generalized eigenvalue decomposition
((Lr, Lr, 1)-GEVD) in order to improve the TDE of the LOS component in challenging scev
narios. The second approach is adaptive processing of individual samples utilizing subspace
tracking to iteratively estimate the subspace at each epoch. Using adaptive processing we
propose two approaches, one applying FBA and ESPS to the data and estimating a higherorder eigenfilter, and the other using a parametric approach using DoA estimation. By extending the data model for an uniform rectangular array, we have a data stream of third-order
tensors. For this model we propose three approaches to TDE based on HOE, CPD-GEVD,
and standard tensor ESPRIT, respectively and employing a sequential truncation strategy to
reduce the amount of operations necessary for each tensor mode
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