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• W2192832501 abstract "This thesis is primarily concerned with developing new parameter based blind deconvolution algorithms and studying their applications. The blind deconvolution problem for minimum phase (MP) systems is well understood, and in this case the well known predictive schemes can be employed. When systems are nonminimum phase (NMP), however, the predictive deconvolution methods can only generate the spectrally equivalent MP solution. This is because the predictive schemes are based only on autocorrelations, which are completely blind to the phase properties of systems. In order to solve the blind deconvolution problem of NMP systems, higher order cumulant (HOC) analysis is adopted in this thesis. The reason for this is that HOC carry the phase information of systems only to a linear phase shift. the parametric approach is adopted due to its advantages in terms of variance and resolution over nonparametric methods. Both MA and AR based models are studied in this work. A new robust blind deconvolution algorithm for MP systems: variance approximation and series decoupling (VASD), is presented first. It is shown that this algorithm possesses some advantages over the existing ones with the same purpose. Then, based on a MA system model, we proposed a HOC-based two-step relay algorithm, in which the close-form formula for MA parameters are combined with an optimal fitting scheme, and the thorny problem of multimodality is overcome to a very great degree. Thus, the optimal identification of the MA parameters of NMP systems can be obtained. In the study of the AR based model, six new families of HOC based linear equations with respect to the AR parameters are derived. Since the inverse filter coefficients are simply the solution of a set of linear equations, their uniqueness can normally be guaranteed. In comparison with the existing AR based methods, only diagonal slices of cumulants are used in our algorithms, in which simplicity and elegance are fully embodied. It has been shown that our algorithm can offer more accurate results than the existing ones. Finally, the algorithms obtained above are made adaptive through the novel use of the successive over-relaxation (SOR) scheme, and a fast convergence rate is realised. As a result, the derived adaptive algorithms are capable of dealing with both linear time invariant (Lii) and linear time variant (LTV) systems. Equalisation of multilevel pulse amplitude modulation (PAM) series, which have been transmitted through NMP channels, is simulated, and the results are presented. The research recorded in this thesis is of interest to both the data communications (NMP channel equalisation) and seismic data processing (NMP wavelet removal) community. LIST OF ABBREVIATIONS AGC automatic gain control AR autoregressive AST asymmetric-to-symmetric transformation ARMA autoregressive moving average BGR Benveniste-Goursat-Ruget DD decision directed DFT discrete Fourier transform EMG electromyogram EW elementary waveform FDM frequency domain multiplexing GM Giannakis-Mendel Giannakis method HOC higher order cumulant HOS higher order spectrum IDFT inverse discrete Fourier transform lID independent and identically distributed 1S1 inter-symbol interference LMS least mean squares LS least squares LTI linear time-invariant LTV linear time-variant MA moving average MC Monte-Carlo MED minimum entropy deconvolution ML maximum likelihood MLD maximum likelihood deconvolution MP minimum phase NMP non-minimum phase NMU neuro-muscular-unit OPT optimal PAM pulse amplitude modulation RLS recursive least squares sEMG surface electromyogram SAT symmetric-to-asymmetric transformation SLP searching linear programming SNR signal-to-noise ratio SOR successive over-relaxation TDM time domain multiplexing TDR time domain reflector VASD variance approximation and series decoupling LIST OF PRINCIPAL SYMBOLS a parameter of AR part in an ARMA model asymmetrically distributed data bi parameter of MA part in a ARMA model c (k) restored series CIO diagonal slice of I th-order cumulants e 2.7182818 e (t) elementary waveform h (k), hk system unit-sample response i parameter number j parameter number V k sample number I parameter number max () maximum of a set of real numbers mm () minimum of a set of real numbers n parameter number n (k) sampled additive (or measurement) noise n (t) additive (or measurement) noise P AR order of ARMA model Pi predictor coefficient q MA order of ARMA model order of non-causal part of inverse filter r2 order of causal part of inverse filter Si symmetrically distributed data S (r) surface electromyogram SC 2 estimated variance t time u• coefficient of elementary waveform w (k) realisation of an RD process x (k) system input series system output series x1 (k) noise-free system output y (k) system output series y, (k) noise-free system output series 9 (k + 1 k) one-step prediction of y (k) at time k Z variable in the z-transform Zi zero of system transfer function A (o) amplitude frequency response of a system C, (T) I th-order cumulant D [] characteristic system" @default.
• W2192832501 created "2016-06-24" @default.
• W2192832501 creator A5016793245 @default.
• W2192832501 date "1992-01-01" @default.
• W2192832501 modified "2023-09-27" @default.
• W2192832501 title "BLIND DECONVOLUTION: TECHNIQUES AND APPLICATIONS" @default.
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