3,276 research outputs found
Best causal mathematical models for a nonlinear system
©2005 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.We provide new causal mathematical models of a nonlinear system S which are specifications of a nonlinear operator P/sub p/ of degree p=1,2,.... The operator P/sub p/ is determined from a special orthogonalization procedure and minimization of the mean squared difference between outputs of S and P/sub p/. As a result, these models have smallest possible associated errors in the class of such operators P/sub p/. The causality condition is implemented through the use of specific matrices called lower trapezoidal. The associated computational work is reduced by the use of the orthogonalization procedure. We provide a strict justification of the proposed approach including theorems on an explicit representatoin of the models' parameters, and theorems on the associated error representation. The possible extensions of the proposed approach and its potential applications are outlined.Anatoli Torokhti, Phil Howlett, and Charles Pearc
Data-Driven Denoising of Stationary Accelerometer Signals
Modern navigation solutions are largely dependent on the performances of the
standalone inertial sensors, especially at times when no external sources are
available. During these outages, the inertial navigation solution is likely to
degrade over time due to instrumental noises sources, particularly when using
consumer low-cost inertial sensors. Conventionally, model-based estimation
algorithms are employed to reduce noise levels and enhance meaningful
information, thus improving the navigation solution directly. However,
guaranteeing their optimality often proves to be challenging as sensors
performance differ in manufacturing quality, process noise modeling, and
calibration precision. In the literature, most inertial denoising models are
model-based when recently several data-driven approaches were suggested
primarily for gyroscope measurements denoising. Data-driven approaches for
accelerometer denoising task are more challenging due to the unknown gravity
projection on the accelerometer axes. To fill this gap, we propose several
learning-based approaches and compare their performances with prominent
denoising algorithms, in terms of pure noise removal, followed by stationary
coarse alignment procedure. Based on the benchmarking results, obtained in
field experiments, we show that: (i) learning-based models perform better than
traditional signal processing filtering; (ii) non-parametric kNN algorithm
outperforms all state of the art deep learning models examined in this study;
(iii) denoising can be fruitful for pure inertial signal reconstruction, but
moreover for navigation-related tasks, as both errors are shown to be reduced
up to one order of magnitude.Comment: 10 pages, 15 figures, 8 table
Adaptive equalisation for fading digital communication channels
This thesis considers the design of new adaptive equalisers for fading digital communication channels. The role of equalisation is discussed in the context of the functions of a digital radio communication system and both conventional and more recent novel equaliser designs are described. The application of recurrent neural networks to the problem of equalisation is developed from a theoretical study of a single node structure to the design of multinode structures. These neural networks are shown to cancel intersymbol interference in a manner mimicking conventional techniques and simulations demonstrate their sensitivity to symbol estimation errors. In addition the error mechanisms of conventional maximum likelihood equalisers operating on rapidly time-varying channels are investigated and highlight the problems of channel estimation using delayed and often incorrect symbol estimates. The relative sensitivity of Bayesian equalisation techniques to errors in the channel estimate is studied and demonstrates that the structure's equalisation capability is also susceptible to such errors. Applications of multiple channel estimator methods are developed, leading to reduced complexity structures which trade performance for a smaller computational load. These novel structures are shown to provide an improvement over the conventional techniques, especially for rapidly time-varying channels, by reducing the time delay in the channel estimation process. Finally, the use of confidence measures of the equaliser's symbol estimates in order to improve channel estimation is studied and isolates the critical areas in the development of the technique — the production of reliable confidence measures by the equalisers and the statistics of symbol estimation error bursts
Final Research Report on Auto-Tagging of Music
The deliverable D4.7 concerns the work achieved by IRCAM until M36 for the “auto-tagging of music”. The deliverable is a research report. The software libraries resulting from the research have been integrated into Fincons/HearDis! Music Library Manager or are used by TU Berlin. The final software libraries are described in D4.5.
The research work on auto-tagging has concentrated on four aspects:
1) Further improving IRCAM’s machine-learning system ircamclass. This has been done by developing the new MASSS audio features, including audio augmentation and audio segmentation into ircamclass. The system has then been applied to train HearDis! “soft” features (Vocals-1, Vocals-2, Pop-Appeal, Intensity, Instrumentation, Timbre, Genre, Style). This is described in Part 3.
2) Developing two sets of “hard” features (i.e. related to musical or musicological concepts) as specified by HearDis! (for integration into Fincons/HearDis! Music Library Manager) and TU Berlin (as input for the prediction model of the GMBI attributes). Such features are either derived from previously estimated higher-level concepts (such as structure, key or succession of chords) or by developing new signal processing algorithm (such as HPSS) or main melody estimation. This is described in Part 4.
3) Developing audio features to characterize the audio quality of a music track. The goal is to describe the quality of the audio independently of its apparent encoding. This is then used to estimate audio degradation or music decade. This is to be used to ensure that playlists contain tracks with similar audio quality. This is described in Part 5.
4) Developing innovative algorithms to extract specific audio features to improve music mixes. So far, innovative techniques (based on various Blind Audio Source Separation algorithms and Convolutional Neural Network) have been developed for singing voice separation, singing voice segmentation, music structure boundaries estimation, and DJ cue-region estimation. This is described in Part 6.EC/H2020/688122/EU/Artist-to-Business-to-Business-to-Consumer Audio Branding System/ABC D
Speech enhancement Algorithm based on super-Gaussian modeling and orthogonal polynomials
© 2020 Lippincott Williams and Wilkins. All rights reserved. Different types of noise from the surrounding always interfere with speech and produce annoying signals for the human auditory system. To exchange speech information in a noisy environment, speech quality and intelligibility must be maintained, which is a challenging task. In most speech enhancement algorithms, the speech signal is characterized by Gaussian or super-Gaussian models, and noise is characterized by a Gaussian prior. However, these assumptions do not always hold in real-life situations, thereby negatively affecting the estimation, and eventually, the performance of the enhancement algorithm. Accordingly, this paper focuses on deriving an optimum low-distortion estimator with models that fit well with speech and noise data signals. This estimator provides minimum levels of speech distortion and residual noise with additional improvements in speech perceptual aspects via four key steps. First, a recent transform based on an orthogonal polynomial is used to transform the observation signal into a transform domain. Second, the noise classification based on feature extraction is adopted to find accurate and mutable models for noise signals. Third, two stages of nonlinear and linear estimators based on the minimum mean square error (MMSE) and new models for speech and noise are derived to estimate a clean speech signal. Finally, the estimated speech signal in the time domain is determined by considering the inverse of the orthogonal transform. The results show that the average classification accuracy of the proposed approach is 99.43%. In addition, the proposed algorithm significantly outperforms existing speech estimators in terms of quality and intelligibility measures
Robust GNSS Carrier Phase-based Position and Attitude Estimation Theory and Applications
Mención Internacional en el título de doctorNavigation information is an essential element for the functioning of robotic platforms and
intelligent transportation systems. Among the existing technologies, Global Navigation Satellite
Systems (GNSS) have established as the cornerstone for outdoor navigation, allowing for
all-weather, all-time positioning and timing at a worldwide scale. GNSS is the generic term
for referring to a constellation of satellites which transmit radio signals used primarily for
ranging information. Therefore, the successful operation and deployment of prospective
autonomous systems is subject to our capabilities to support GNSS in the provision of
robust and precise navigational estimates.
GNSS signals enable two types of ranging observations: –code pseudorange, which is a
measure of the time difference between the signal’s emission and reception at the satellite
and receiver, respectively, scaled by the speed of light; –carrier phase pseudorange, which
measures the beat of the carrier signal and the number of accumulated full carrier cycles.
While code pseudoranges provides an unambiguous measure of the distance between satellites
and receiver, with a dm-level precision when disregarding atmospheric delays and clock offsets,
carrier phase measurements present a much higher precision, at the cost of being ambiguous by
an unknown number of integer cycles, commonly denoted as ambiguities. Thus, the maximum
potential of GNSS, in terms of navigational precision, can be reach by the use of carrier phase
observations which, in turn, lead to complicated estimation problems.
This thesis deals with the estimation theory behind the provision of carrier phase-based
precise navigation for vehicles traversing scenarios with harsh signal propagation conditions.
Contributions to such a broad topic are made in three directions. First, the ultimate positioning
performance is addressed, by proposing lower bounds on the signal processing realized at the
receiver level and for the mixed real- and integer-valued problem related to carrier phase-based
positioning. Second, multi-antenna configurations are considered for the computation of a
vehicle’s orientation, introducing a new model for the joint position and attitude estimation
problems and proposing new deterministic and recursive estimators based on Lie Theory.
Finally, the framework of robust statistics is explored to propose new solutions to code- and
carrier phase-based navigation, able to deal with outlying impulsive noises.La información de navegación es un elemental fundamental para el funcionamiento de sistemas
de transporte inteligentes y plataformas robóticas. Entre las tecnologías existentes, los
Sistemas Globales de Navegación por Satélite (GNSS) se han consolidado como la piedra
angular para la navegación en exteriores, dando acceso a localización y sincronización temporal
a una escala global, irrespectivamente de la condición meteorológica. GNSS es el término
genérico que define una constelación de satélites que transmiten señales de radio, usadas
primordinalmente para proporcionar información de distancia. Por lo tanto, la operatibilidad y
funcionamiento de los futuros sistemas autónomos pende de nuestra capacidad para explotar
GNSS y estimar soluciones de navegación robustas y precisas.
Las señales GNSS permiten dos tipos de observaciones de alcance: –pseudorangos de
código, que miden el tiempo transcurrido entre la emisión de las señales en los satélites y su
acquisición en la tierra por parte de un receptor; –pseudorangos de fase de portadora, que
miden la fase de la onda sinusoide que portan dichas señales y el número acumulado de ciclos
completos. Los pseudorangos de código proporcionan una medida inequívoca de la distancia
entre los satélites y el receptor, con una precisión de decímetros cuando no se tienen en
cuenta los retrasos atmosféricos y los desfases del reloj. En contraposición, las observaciones
de la portadora son super precisas, alcanzando el milímetro de exactidud, a expensas de ser
ambiguas por un número entero y desconocido de ciclos. Por ende, el alcanzar la máxima
precisión con GNSS queda condicionado al uso de las medidas de fase de la portadora, lo
cual implica unos problemas de estimación de elevada complejidad.
Esta tesis versa sobre la teoría de estimación relacionada con la provisión de navegación
precisa basada en la fase de la portadora, especialmente para vehículos que transitan escenarios
donde las señales no se propagan fácilmente, como es el caso de las ciudades. Para ello,
primero se aborda la máxima efectividad del problema de localización, proponiendo cotas
inferiores para el procesamiento de la señal en el receptor y para el problema de estimación
mixto (es decir, cuando las incógnitas pertenecen al espacio de números reales y enteros). En
segundo lugar, se consideran las configuraciones multiantena para el cálculo de la orientación de un vehículo, presentando un nuevo modelo para la estimación conjunta de posición y
rumbo, y proponiendo estimadores deterministas y recursivos basados en la teoría de Lie. Por
último, se explora el marco de la estadística robusta para proporcionar nuevas soluciones de
navegación precisa, capaces de hacer frente a los ruidos atípicos.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José Manuel Molina López.- Secretario: Giorgi Gabriele.- Vocal: Fabio Dovi
Novel Computational Methods for State Space Filtering
The state-space formulation for time-dependent models has been long used invarious applications in science and engineering. While the classical Kalman filter(KF) provides optimal posterior estimation under linear Gaussian models, filteringin nonlinear and non-Gaussian environments remains challenging.Based on the Monte Carlo approximation, the classical particle filter (PF) can providemore precise estimation under nonlinear non-Gaussian models. However, it suffers fromparticle degeneracy. Drawing from optimal transport theory, the stochastic map filter(SMF) accommodates a solution to this problem, but its performance is influenced bythe limited flexibility of nonlinear map parameterisation. To account for these issues,a hybrid particle-stochastic map filter (PSMF) is first proposed in this thesis, wherethe two parts of the split likelihood are assimilated by the PF and SMF, respectively.Systematic resampling and smoothing are employed to alleviate the particle degeneracycaused by the PF. Furthermore, two PSMF variants based on the linear and nonlinearmaps (PSMF-L and PSMF-NL) are proposed, and their filtering performance is comparedwith various benchmark filters under different nonlinear non-Gaussian models.Although achieving accurate filtering results, the particle-based filters require expensive computations because of the large number of samples involved. Instead, robustKalman filters (RKFs) provide efficient solutions for the linear models with heavy-tailednoise, by adopting the recursive estimation framework of the KF. To exploit the stochasticcharacteristics of the noise, the use of heavy-tailed distributions which can fit variouspractical noises constitutes a viable solution. Hence, this thesis also introduces a novelRKF framework, RKF-SGαS, where the signal noise is assumed to be Gaussian and theheavy-tailed measurement noise is modelled by the sub-Gaussian α-stable (SGαS) distribution. The corresponding joint posterior distribution of the state vector and auxiliaryrandom variables is estimated by the variational Bayesian (VB) approach. Four differentminimum mean square error (MMSE) estimators of the scale function are presented.Besides, the RKF-SGαS is compared with the state-of-the-art RKFs under three kinds ofheavy-tailed measurement noises, and the simulation results demonstrate its estimationaccuracy and efficiency.One notable limitation of the proposed RKF-SGαS is its reliance on precise modelparameters, and substantial model errors can potentially impede its filtering performance. Therefore, this thesis also introduces a data-driven RKF method, referred to asRKFnet, which combines the conventional RKF framework with a deep learning technique. An unsupervised scheduled sampling technique (USS) is proposed to improve theistability of the training process. Furthermore, the advantages of the proposed RKFnetare quantified with respect to various traditional RKFs
Bayesian reconstruction of the cosmological large-scale structure: methodology, inverse algorithms and numerical optimization
We address the inverse problem of cosmic large-scale structure reconstruction
from a Bayesian perspective. For a linear data model, a number of known and
novel reconstruction schemes, which differ in terms of the underlying signal
prior, data likelihood, and numerical inverse extra-regularization schemes are
derived and classified. The Bayesian methodology presented in this paper tries
to unify and extend the following methods: Wiener-filtering, Tikhonov
regularization, Ridge regression, Maximum Entropy, and inverse regularization
techniques. The inverse techniques considered here are the asymptotic
regularization, the Jacobi, Steepest Descent, Newton-Raphson,
Landweber-Fridman, and both linear and non-linear Krylov methods based on
Fletcher-Reeves, Polak-Ribiere, and Hestenes-Stiefel Conjugate Gradients. The
structures of the up-to-date highest-performing algorithms are presented, based
on an operator scheme, which permits one to exploit the power of fast Fourier
transforms. Using such an implementation of the generalized Wiener-filter in
the novel ARGO-software package, the different numerical schemes are
benchmarked with 1-, 2-, and 3-dimensional problems including structured white
and Poissonian noise, data windowing and blurring effects. A novel numerical
Krylov scheme is shown to be superior in terms of performance and fidelity.
These fast inverse methods ultimately will enable the application of sampling
techniques to explore complex joint posterior distributions. We outline how the
space of the dark-matter density field, the peculiar velocity field, and the
power spectrum can jointly be investigated by a Gibbs-sampling process. Such a
method can be applied for the redshift distortions correction of the observed
galaxies and for time-reversal reconstructions of the initial density field.Comment: 40 pages, 11 figure
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