Noise or interference is often assumed to be a random process. Conventional linear filtering, control or prediction techniques are used to cancel or reduce the noise. However, some noise processes have been shown to be nonlinear and deterministic. These nonlinear deterministic noise processes appear to be random when analysed with second order statistics. As nonlinear processes are widespread in nature it may be beneficial to exploit the coherence of the nonlinear deterministic noise with nonlinear filtering techniques. The nonlinear deterministic noise processes used in this thesis are generated from nonlinear difference or differential equations which are derived from real world scenarios. Analysis tools from the theory of nonlinear dynamics are used to determine an appropriate sampling rate of the nonlinear deterministic noise processes and their embedding dimensions. Nonlinear models, such as the Volterra series filter and the radial basis function ...

Radar backscatter from an ocean surface is commonly referred to as sea clutter. Any radar backscatter not due to the scattering from an ocean surface constitutes a potential target. This thesis is concerned with the study of target detection techniques in the presence of high resolution sea clutter. In this dissertation, the high resolution sea clutter is treated as a compound process, where a fast oscillating speckle component is modulated in power by a slowly varying modulating component. While the short term temporal correlations of the clutter are associated with the speckle, the spatial correlations are largely associated with the modulating component. Due to the disparate statistical and correlation properties of the two components, a piecemeal approach is adopted throughout this thesis, whereby the spatial and the temporal correlations of high resolution sea clutter are treated independently. As an extension ...

Sigma delta modulation has become a widespread method of analogue to digital conversion, however its operation has not been completely defined. The majority of the analysis carried out on the circuit has been from a linear standpoint, with non-linear analysis hinting at hidden complexities in the modulator’s operation. The sigma delta modulator itself is a non-linear system consisting, as it does, of a number of integrators and a one bit quantiser in a feedback loop. This configuration can be generalised as a non-linearity within a feedback path, which is a classic route to chaotic behaviour. This initially raises the prospect that a sigma delta modulator may be capable of chaotic modes of operation with a non-chaotic input. In fact, the problem does not arise and we show why not. To facilitate this investigation, a set of differential equations is formulated ...

In this thesis we examine the autonomous oscillator model for synthesis of speech signals. The contributions comprise an analysis of realizations and training methods for the nonlinear function used in the oscillator model, the combination of the oscillator model with inverse filtering, both significantly increasing the number of `successfully' re-synthesized speech signals, and the introduction of a new technique suitable for the re-generation of the noise-like signal component in speech signals. Nonlinear function models are compared in a one-dimensional modeling task regarding their presupposition for adequate re-synthesis of speech signals, in particular considering stability. The considerations also comprise the structure of the nonlinear functions, with the aspect of the possible interpolation between models for different speech sounds. Both regarding stability of the oscillator and the premiss of a nonlinear function structure that may be pre-defined, RBF networks are found a ...

This thesis reports on an investigation of various system architectures and receiver structures for cellular communications systems which discriminate users by direct sequence code division multiple access (DSCDMA). Attention is focussed on the downlink of such a spread spectrum system and the influence of a number of design parameters is considered. The objective of the thesis is to investigate signal processing techniques which may be employed either at the receiver, or throughout the system to improve the overall capacity. The principles of spread spectrum communication are first outlined, including a discussion of the relative merits of spreading sequence sets, and a description of various signal processing techniques which are to be applied to the multi-user environment. The measure of system performance is introduced, and the conventional DS-CDMA system is analysed theoretically and through simulation to provide a reference performance level. ...

A large part of human speech communication takes place in noisy environments and is supported by technical devices. For example, a hearing-impaired person might use a hearing aid to take part in a conversation in a busy restaurant. These devices, but also telecommunication in noisy environments or voiced-controlled assistants, make use of speech enhancement and separation algorithms that improve the quality and intelligibility of speech by separating speakers and suppressing background noise as well as other unwanted effects such as reverberation. If the devices are equipped with more than one microphone, which is very common nowadays, then multi-channel speech enhancement approaches can leverage spatial information in addition to single-channel tempo-spectral information to perform the task. Traditionally, linear spatial filters, so-called beamformers, have been employed to suppress the signal components from other than the target direction and thereby enhance the desired ...

Given the widespread use of miniaturized audio interfaces, echo control systems are faced with increasing challenges to address a large variety of acoustic conditions observed by such interfaces. This motivates the use of sophisticated machine learning-based techniques to overcome the limitations of conventional methods. The contributions in this thesis can be outlined by decomposing the task of nonlinear acoustic echo control into two subtasks: Nonlinear Acoustic Echo Cancellation (NAEC) and Acoustic Echo Suppression (AES). In particular, by formulating the single-channel NAEC model-adaptation task as a Bayesian recursive filtering problem, an evolutionary resampling strategy for particle filtering is proposed. The resulting Elitist Resampling Particle Filter (ERPF) is shown experimentally to be an efficient and high-performing approach that can be extended to address challenging conditions such as non-stationary interferers. The fundamental problem of nonlinear model design is addressed by proposing a novel ...

Digital subscriber line (DSL) technology is a very popular broadband access technology. It uses the existing telephone infrastructure to provide broadband access. In order to cope with the increased bandwidth demand to support broadband services, such as, Video on Demand (VoD), real time multimedia streaming, it is important to further improve the DSL. The main performance degradation of the DSL system is caused by channel impairments, such as, crosstalk and inter-symbol interference (ISI). Furthermore, the discrete Fourier transform (DFT) based discrete multitone (DMT) system has very poor spectral properties, which prohibit the use of tones at the band edges in order to meet the power spectral density (PSD) constraints of the system, thus reducing the achievable bit rate. In order to mitigate the channel impairments as well as to combat the poor spectral properties of the DFT based DMT, sophisticated ...

Techniques which utilise a signal’s Higher Order Statistics (HOS) can reveal information about non-Gaussian signals and nonlinearities which cannot be obtained using conventional (second-order) techniques. This information may be useful in speech processing because it may provide clues about how to construct new models of speech production which are better than existing models. There has been a recent surge of interest in the application of HOS techniques to speech processing, but this has been handicapped by a lack of understanding of what the HOS properties of speech signals are. Without this understanding the HOS information which is in speech signals can not be efficiently utilised. This thesis describes an investigation into the use of HOS techniques, in particular the third-order frequency domain measure called the bispectrum, to speech signals Several issues relating to bispectral speech analysis are addressed; including nonlinearity ...

Understanding speech in noise and the occurrence of acoustic feedback belong to the major problems of current hearing aid users. Hence, an urgent demand exists for efficient and well-working digital signal processing algorithms that offer a solution to these issues. In this thesis we develop adaptive filtering techniques for noise reduction and acoustic feedback cancellation. Thanks to the availability of low power digital signal processors, these algorithms can be integrated in a hearing aid. Because of the ongoing miniaturization in the hearing aid industry and the growing tendency towards multi-microphone hearing aids, robustness against imperfections such as microphone mismatch, has become a major issue in the design of a noise reduction algorithm. In this thesis we propose multimicrophone noise reduction techniques that are based on multi-channel Wiener filtering (MWF). Theoretical and experimental analysis demonstrate that these MWF-based techniques are less ...

Channel equalisers are used in digital communication receivers to mitigate the effects of inter symbol interference (ISI) and inter user interference in the form of co-channel interference (CCI) and adjacent channel interference (ACI) in the presence of additive white Gaussian noise (AWGN). An equaliser uses a large part of the computations involved in the receiver. Linear equalisers based on adaptive filtering techniques have long been used for this application. Recently, use of nonlinear signal processing techniques like artificial neural networks (ANN) and radial basis functions (RBF) have shown encouraging results in this application. This thesis presents the development of a nonlinear fuzzy system based equaliser for digital communication receivers. The fuzzy equaliser proposed in this thesis provides a parametric implementation of symbolby- symbol maximum a-posteriori probability (MAP) equaliser based on Bayes’s theory. This MAP equaliser is also called Bayesian equaliser. ...

Today, adaptive algorithms represent one of the most frequently used computational tools for the processing of digital speech signals. This work investigates and analyzes the properties of adaptive algorithms in speech communication applications where rigorous conditions apply, such as noise and echo cancelation. Like other theses in this field do, it tries to tackle the ever-lasting problem of computational complexity vs. rate of convergence. It introduces some new adaptive methods that stem from the existing algorithms as well as a novel concept which has been entitled Optimal Step-Size (OSS). In the first part of the thesis we investigate some well-known, widely used adaptive techniques such as the Normalized Least Mean Squares (NLMS) and the Recursive Least Mean Squares (RLS). In spite of the fact that the NLMS and the RLS belong to the "simplest" principles, as far as complexity is ...

This thesis deals with estimation of states and parameters in nonlinear and non-Gaussian dynamic systems. Sequential Monte Carlo methods are mainly used to this end. These methods rely on models of the underlying system, motivating some developments of the model concept. One of the main reasons for the interest in nonlinear estimation is that problems of this kind arise naturally in many important applications. Several applications of nonlinear estimation are studied. The models most commonly used for estimation are based on stochastic difference equations, referred to as state-space models. This thesis is mainly concerned with models of this kind. However, there will be a brief digression from this, in the treatment of the mathematically more intricate differential-algebraic equations. Here, the purpose is to write these equations in a form suitable for statistical signal processing. The nonlinear state estimation problem is ...

Multiuser demodulation algorithms for centralized receivers of asynchronous direct-sequence (DS) spread-spectrum code-division multiple-access (CDMA) systems in frequency-selective fading channels are studied. Both DS-CDMA systems with short (one symbol interval) and long (several symbol intervals) spreading sequences are considered. Linear multiuser receivers process ideally the complete received data block. The approximation of ideal infinite memory-length (IIR) linear multiuser detectors by finite memory-length (FIR) detectors is studied. It is shown that the FIR detectors can be made near-far resistant under a given ratio between maximum and minimum received power of users by selecting an appropriate memory-length. Numerical examples demonstrate the fact that moderate memory-lengths of the FIR detectors are sufficient to achieve the performance of the ideal IIR detectors even under severe near-far conditions. Multiuser demodulation in relatively fast fading channels is analyzed. The optimal maximum likelihood sequence detection receiver and suboptimal ...

Spectral unmixing is one the major issues arising when analysing hyperspectral images. It consists of identifying the macroscopic materials present in a hyperspectral image and quantifying the proportions of these materials in the image pixels. Most unmixing techniques rely on a linear mixing model which is often considered as a first approximation of the actual mixtures. However, the linear model can be inaccurate for some specific images (for instance images of scenes involving multiple reflections) and more complex nonlinear models must then be considered to analyse such images. The aim of this thesis is to study new nonlinear mixing models and to propose associated algorithms to analyse hyperspectral images. First, a post-nonlinear model is investigated and efficient unmixing algorithms based on this model are proposed. The prior knowledge about the components present in the observed image, their proportions and the ...