A robust orthogonal adaptive approach to SISO deconvolution

This paper formulates in a common framework some results from the fields of robust filtering, function approximation with orthogonal basis, and adaptive filtering, and applies them for the design of a general deconvolution processor for SISO systems. The processor is designed to be robust to small parametric uncertainties in the system model, with a partially adaptive orthogonal structure. A simple gradient type of adaptive algorithm is applied to update the coefficients that linearly combine the fixed robust basis functions used to represent the deconvolver. The advantages of the design are inherited from the mentioned fields: low sensitivity to parameter uncertainty in the system model, good numerical and structural behaviour, and the capability of tracking changes in the systems dynamics. The linear equalization of a simple ADSL channel model is presented as an example including comparisons between the optimal nominal, adaptive FIR, and the proposed design.Facultad de IngenieríaComisión de Investigaciones Científicas de la provincia de Buenos Aire

On adaptive filter structure and performance

SIGLEAvailable from British Library Document Supply Centre- DSC:D75686/87 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Adaptive IIR filtering using the homotopy continuation method

The objective of this study is to develop an algorithmic approach for solving problems associated with the convergence to the local minima in adaptive IIR filtering. The approach is based on a numerical method called the homotopy continuation method;The homotopy continuation method is a solution exhaustive method for calculating all solutions of a set of nonlinear equations. The globally optimum filter coefficients correspond to the solutions with minimum mean square error. In order to apply the technique to the adaptive IIR filtering problem, the homotopy continuation method is modified to handle a set of nonlinear polynomials with time-varying coefficients. Then, the adaptive IIR filtering problem is formulated in terms of a set of nonlinear polynomials using the mean square output error minimization approach. The adaptive homotopy continuation method (AHCM) for the case of time-varying coefficients is then applied to solve the IIR filtering problem. After demonstrating the feasibility of the approach, problems encountered in the basic AHCM algorithm are discussed and alternative structures of the filter are proposed. In the development of the proposed algorithm and its variations, the instability problem which is a second disadvantage of IIR filters is also considered;Simulation results for a system identification example validate the proposed algorithm by determining the filter coefficients at the global minimum position. For further validation, the AHCM algorithm is then applied to an adaptive noise cancellation application in ultrasonic nondestructive evaluation. Ultrasonic inspection signal reflections from defects and material grain boundaries are considered. The AHCM algorithm is applied to the noise cancellation mode to filter out the material noise. The experimental results show that the proposed algorithm shows considerable promise for real as well as for simulated data

Reduced order strip Kalman filtering using singular perturbation method

Includes bibliographical references.Strip Kalman filtering for restoration of images degraded by linear shift invariant (LSI) blur and additive white Gaussian (WG) noise is considered. The image process is modeled by a 1-D vector autoregressive (AR) model in each strip. It is shown that the composite dynamic model that is obtained by combining the image model and the blur model takes the form of a singularly perturbed system owing to the strong-weak correlation effects within a window. The time scale property of the singularly perturbed system is then utilized to decompose the original system into reduced order subsystems which closely capture the behavior of the full order system. For these subsystems the relevant Kalman filtering equations are given which provide the suboptimal filtered estimates of the image and the one-step prediction estimates of the blur needed for the next stage. Simulation results are also provided

Frequency-warped autoregressive modeling and filtering

This thesis consists of an introduction and nine articles. The articles are related to the application of frequency-warping techniques to audio signal processing, and in particular, predictive coding of wideband audio signals. The introduction reviews the literature and summarizes the results of the articles. Frequency-warping, or simply warping techniques are based on a modification of a conventional signal processing system so that the inherent frequency representation in the system is changed. It is demonstrated that this may be done for basically all traditional signal processing algorithms. In audio applications it is beneficial to modify the system so that the new frequency representation is close to that of human hearing. One of the articles is a tutorial paper on the use of warping techniques in audio applications. Majority of the articles studies warped linear prediction, WLP, and its use in wideband audio coding. It is proposed that warped linear prediction would be particularly attractive method for low-delay wideband audio coding. Warping techniques are also applied to various modifications of classical linear predictive coding techniques. This was made possible partly by the introduction of a class of new implementation techniques for recursive filters in one of the articles. The proposed implementation algorithm for recursive filters having delay-free loops is a generic technique. This inspired to write an article which introduces a generalized warped linear predictive coding scheme. One example of the generalized approach is a linear predictive algorithm using almost logarithmic frequency representation.reviewe

A robust orthogonal adaptive approach to SISO deconvolution

This paper formulates in a common framework some results from the fields of robust filtering, function approximation with orthogonal basis, and adaptive filtering, and applies them for the design of a general deconvolution processor for SISO systems. The processor is designed to be robust to small parametric uncertainties in the system model, with a partially adaptive orthogonal structure. A simple gradient type of adaptive algorithm is applied to update the coefficients that linearly combine the fixed robust basis functions used to represent the deconvolver. The advantages of the design are inherited from the mentioned fields: low sensitivity to parameter uncertainty in the system model, good numerical and structural behaviour, and the capability of tracking changes in the systems dynamics. The linear equalization of a simple ADSL channel model is presented as an example including comparisons between the optimal nominal, adaptive FIR, and the proposed design.Facultad de IngenieríaComisión de Investigaciones Científicas de la provincia de Buenos Aire

Laboratory for Engineering Man/Machine Systems (LEMS): System identification, model reduction and deconvolution filtering using Fourier based modulating signals and high order statistics

Several important problems in the fields of signal processing and model identification, such as system structure identification, frequency response determination, high order model reduction, high resolution frequency analysis, deconvolution filtering, and etc. Each of these topics involves a wide range of applications and has received considerable attention. Using the Fourier based sinusoidal modulating signals, it is shown that a discrete autoregressive model can be constructed for the least squares identification of continuous systems. Some identification algorithms are presented for both SISO and MIMO systems frequency response determination using only transient data. Also, several new schemes for model reduction were developed. Based upon the complex sinusoidal modulating signals, a parametric least squares algorithm for high resolution frequency estimation is proposed. Numerical examples show that the proposed algorithm gives better performance than the usual. Also, the problem was studied of deconvolution and parameter identification of a general noncausal nonminimum phase ARMA system driven by non-Gaussian stationary random processes. Algorithms are introduced for inverse cumulant estimation, both in the frequency domain via the FFT algorithms and in the domain via the least squares algorithm

Some fast algorithms in signal and image processing.

Kwok-po Ng.Thesis (Ph.D.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 138-139).AbstractsSummaryIntroduction --- p.1Summary of the papers A-F --- p.2Paper A --- p.15Paper B --- p.36Paper C --- p.63Paper D --- p.87Paper E --- p.109Paper F --- p.12