Efficient Adaptive Filter Algorithms Using Variable Tap-length Scheme

Today the usage of digital signal processors has increased, where adaptive filter algorithms are now routinely employed in mostly all contemporary devices such as mobile phones, camcorders, digital cameras, and medical monitoring equipment, to name few. The filter tap-length, or the number of taps, is a significant structural parameter of adaptive filters that can influences both the complexity and steady-state performance characteristics of the filter. Traditional implementation of adaptive filtering algorithms presume some fixed filter-length and focus on estimating variable filter\u27s tap-weights parameters according to some pre-determined cost function. Although this approach can be adequate in some applications, it is not the case in more complicated ones as it does not answer the question of filter size (tap-length). This problem can be more apparent when the application involves a change in impulse response, making it hard for the adaptive filter algorithm to achieve best potential performance. A cost-effective approach is to come up with variable tap-length filtering scheme that can search for the optimal length while the filter is adapting its coefficients. In direct form structure filtering, commonly known as a transversal adaptive filter, several schemes were used to estimate the optimum tap-length. Among existing algorithms, pseudo fractional tap-length (FT) algorithm, is of particular interest because of its fast convergence rate and small steady-state error. Lattice structured adaptive filters, on the other hand, have attracted attention recently due to a number of desirable properties. The aim of this research is to develop efficient adaptive filter algorithms that fill the gap where optimal filter structures were not proposed by incorporating the concept of pseudo fractional tap-length (FT) in adaptive filtering algorithms. The contribution of this research include the development of variable length adaptive filter scheme and hence optimal filter structure for the following applications: (1) lattice prediction; (2) Least-Mean-Squares (LMS) lattice system identification; (3) Recursive Least-Squares (RLS) lattice system identification; (4) Constant Modulus Algorithm (CMA) blind equalization. To demonstrate the capability of proposed algorithms, simulations examples are implemented in different experimental conditions, where the results showed noticeable improvement in the context of mean square Error (MSE), as well as in the context of convergence rate of the proposed algorithms with their counterparts adaptive filter algorithms. Simulation results have also proven that with affordable extra computational complexity, an optimization for both of the adaptive filter coefficients and the filter tap-length can be attained

An investigation into the performance of a power-of-two coefficient transversal equalizer in a 34Mbit/s QPSK digital radio during frequency-selective fading conditions

Bibliography: leaves 82-91.Under certain atmospheric conditions, multipath propagation can occur. The interaction of radio waves arriving at a receiver, having travelled via paths of differing length, results in the phenomenon of frequency-selective fading. This phenomenon manifests as a notch in the received spectrum and causes a severe degradation in the performance of a digital radio system. As the total power in the received bandwidth may be unaffected, the Automatic Gain Control is not able to correct for this distortion, and so other methods are required. The dissertation commences with a summary of the phenomenon of multipath as this provides the context for the investigations which follow. The adaptive equalizer was developed to combat the distortion introduced by frequency-selective fading. It achieves this by applying an estimate of the inverse of the distorting channel's transfer function. The theory on adaptive equalizers has been well established, and a summary of this theory is presented in the form of Wiener Filter theory and the Wiener-Hopf equations. An adaptive equalizer located in a 34MBit/s QPSK digital radio is required to operate at very high speed, and its digital hardware implementation is not a trivial task. In order to reduce the cost and complexity, a compromise was proposed. If the tap weights of the equalizer could be represented by power-of-two binary numbers, the equalizer circuitry can be dramatically simplified. The aim of the dissertation was to investigate the performance of this simplified equalizer structure and to determine whether a power-of-two equalizer was a viable consideration

Broadband adaptive beamforming with low complexity and frequency invariant response

This thesis proposes different methods to reduce the computational complexity as well as increasing the adaptation rate of adaptive broadband beamformers. This is performed exemplarily for the generalised sidelobe canceller (GSC) structure. The GSC is an alternative implementation of the linearly constrained minimum variance beamformer, which can utilise well-known adaptive filtering algorithms, such as the least mean square (LMS) or the recursive least squares (RLS) to perform unconstrained adaptive optimisation.A direct DFT implementation, by which broadband signals are decomposed into frequency bins and processed by independent narrowband beamforming algorithms, is thought to be computationally optimum. However, this setup fail to converge to the time domain minimum mean square error (MMSE) if signal components are not aligned to frequency bins, resulting in a large worst case error. To mitigate this problem of the so-called independent frequency bin (IFB) processor, overlap-save based GSC beamforming structures have been explored. This system address the minimisation of the time domain MMSE, with a significant reduction in computational complexity when compared to time-domain implementations, and show a better convergence behaviour than the IFB beamformer. By studying the effects that the blocking matrix has on the adaptive process for the overlap-save beamformer, several modifications are carried out to enhance both the simplicity of the algorithm as well as its convergence speed. These modifications result in the GSC beamformer utilising a significantly lower computational complexity compare to the time domain approach while offering similar convergence characteristics.In certain applications, especially in the areas of acoustics, there is a need to maintain constant resolution across a wide operating spectrum that may extend across several octaves. To attain constant beamwidth is difficult, particularly if uniformly spaced linear sensor array are employed for beamforming, since spatial resolution is reciprocally proportional to both the array aperture and the frequency. A scaled aperture arrangement is introduced for the subband based GSC beamformer to achieve near uniform resolution across a wide spectrum, whereby an octave-invariant design is achieved. This structure can also be operated in conjunction with adaptive beamforming algorithms. Frequency dependent tapering of the sensor signals is proposed in combination with the overlap-save GSC structure in order to achieve an overall frequency-invariant characteristic. An adaptive version is proposed for frequency-invariant overlap-save GSC beamformer. Broadband adaptive beamforming algorithms based on the family of least mean squares (LMS) algorithms are known to exhibit slow convergence if the input signal is correlated. To improve the convergence of the GSC when based on LMS-type algorithms, we propose the use of a broadband eigenvalue decomposition (BEVD) to decorrelate the input of the adaptive algorithm in the spatial dimension, for which an increase in convergence speed can be demonstrated over other decorrelating measures, such as the Karhunen-Loeve transform. In order to address the remaining temporal correlation after BEVD processing, this approach is combined with subband decomposition through the use of oversampled filter banks. The resulting spatially and temporally decorrelated GSC beamformer provides further enhanced convergence speed over spatial or temporal decorrelation methods on their own

Adaptive techniques for signal enhancement in the human electroencephalogram

This thesis describes an investigation of adaptive noise cancelling applied to human brain evoked potentials (EPs), with particular emphasis on visually evoked responses. The chief morphological features and signal properties of EPs are described. Consideration is given to the amplitude and spectral properties of the underlying spontaneous electroencephalogram and the importance of noise reduction techniques in EP studies is empnasised. A number of methods of enhancing EP waveforms are reviewed in the light of the known limitations of coherent signal averaging. These are shown to oe generally inadequate for enhancing individual EP responses. The theory of adaptive filters is reviewed with particular reference to adaptive transversal filters usiny the Widrow-Hoff algorithm. The theory of adaptive noise cancelling using correlated reference sources is presented, and new work is described which relates canceller performance to the magnitude-squared coherence function of the input signals. A novel filter structure, the gated adaptive filter, is presented and shown to yield improved cancellation without signal distortion when applied to repetitive transient signals in stationary noise under the condition of fast adaption. The signal processing software available is shown to be inadequate, and a comprehensive Fortran program developed for use on a PDP-11 computer is described. The properties of human visual evoked potentials and the EEO are investigated in two normal adults using a montage of 7 occipital electrodes. Signal enhancement of EPs is shown to be possible oy adaptive noise cancelling, and improvements in signal to noise in the range 2-10 dB are predicted. A discussion of filter strategies is presented, and a detailed investiyation of adaptive noise cancel liny performed usiny a ranye of typical EP data. Assessment of the results confirms the proposal that substantial improvement in sinyle EP response recoynition is achieved by this technique

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

Motion Artifact Processing Techniques for Physiological Signals

The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an ageing population and this is placing an ever-increasing burden on our healthcare systems. The urgent need to address this so called healthcare \time bomb" has led to a rapid growth in research into ubiquitous, pervasive and distributed healthcare technologies where recent advances in signal acquisition, data storage and communication are helping such systems become a reality. However, similar to recordings performed in the hospital environment, artifacts continue to be a major issue for these systems. The magnitude and frequency of artifacts can vary signicantly depending on the recording environment with one of the major contributions due to the motion of the subject or the recording transducer. As such, this thesis addresses the challenges of the removal of this motion artifact removal from various physiological signals. The preliminary investigations focus on artifact identication and the tagging of physiological signals streams with measures of signal quality. A new method for quantifying signal quality is developed based on the use of inexpensive accelerometers which facilitates the appropriate use of artifact processing methods as needed. These artifact processing methods are thoroughly examined as part of a comprehensive review of the most commonly applicable methods. This review forms the basis for the comparative studies subsequently presented. Then, a simple but novel experimental methodology for the comparison of artifact processing techniques is proposed, designed and tested for algorithm evaluation. The method is demonstrated to be highly eective for the type of artifact challenges common in a connected health setting, particularly those concerned with brain activity monitoring. This research primarily focuses on applying the techniques to functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) data due to their high susceptibility to contamination by subject motion related artifact. Using the novel experimental methodology, complemented with simulated data, a comprehensive comparison of a range of artifact processing methods is conducted, allowing the identication of the set of the best performing methods. A novel artifact removal technique is also developed, namely ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA), which provides the best results when applied on fNIRS data under particular conditions. Four of the best performing techniques were then tested on real ambulatory EEG data contaminated with movement artifacts comparable to those observed during in-home monitoring. It was determined that when analysing EEG data, the Wiener lter is consistently the best performing artifact removal technique. However, when employing the fNIRS data, the best technique depends on a number of factors including: 1) the availability of a reference signal and 2) whether or not the form of the artifact is known. It is envisaged that the use of physiological signal monitoring for patient healthcare will grow signicantly over the next number of decades and it is hoped that this thesis will aid in the progression and development of artifact removal techniques capable of supporting this growth