Recursive Estimation Of Linear Systems' Parameters Based On Cumulant Matching

The problem of the estimation of the parameters of linear systems from noisy inputoutput measurements is considered. A third-order cumulant matching recursive algorithm is developed. The algorithm provides unbiased estimates of the parameters for a wide class of correlated noise corrupting both the input and the output measurements. A Monte Carlo type of simulation shows the consistency, and the superiority of the developed algorithm over the least-squares techniqu

Recursive Estimation Of Linear Systems' Parameters Based On Cumulant Matching

The problem of the estimation of the parameters of linear systems from noisy inputoutput measurements is considered. A third-order cumulant matching recursive algorithm is developed. The algorithm provides unbiased estimates of the parameters for a wide class of correlated noise corrupting both the input and the output measurements. A Monte Carlo type of simulation shows the consistency, and the superiority of the developed algorithm over the least-squares techniqu

Seismic Ray Impedance Inversion

This thesis investigates a prestack seismic inversion scheme implemented in the ray parameter domain. Conventionally, most prestack seismic inversion methods are performed in the incidence angle domain. However, inversion using the concept of ray impedance, as it honours ray path variation following the elastic parameter variation according to Snell’s law, shows the capacity to discriminate different lithologies if compared to conventional elastic impedance inversion. The procedure starts with data transformation into the ray-parameter domain and then implements the ray impedance inversion along constant ray-parameter profiles. With different constant-ray-parameter profiles, mixed-phase wavelets are initially estimated based on the high-order statistics of the data and further refined after a proper well-to-seismic tie. With the estimated wavelets ready, a Cauchy inversion method is used to invert for seismic reflectivity sequences, aiming at recovering seismic reflectivity sequences for blocky impedance inversion. The impedance inversion from reflectivity sequences adopts a standard generalised linear inversion scheme, whose results are utilised to identify rock properties and facilitate quantitative interpretation. It has also been demonstrated that we can further invert elastic parameters from ray impedance values, without eliminating an extra density term or introducing a Gardner’s relation to absorb this term. Ray impedance inversion is extended to P-S converted waves by introducing the definition of converted-wave ray impedance. This quantity shows some advantages in connecting prestack converted wave data with well logs, if compared with the shearwave elastic impedance derived from the Aki and Richards approximation to the Zoeppritz equations. An analysis of P-P and P-S wave data under the framework of ray impedance is conducted through a real multicomponent dataset, which can reduce the uncertainty in lithology identification.Inversion is the key method in generating those examples throughout the entire thesis as we believe it can render robust solutions to geophysical problems. Apart from the reflectivity sequence, ray impedance and elastic parameter inversion mentioned above, inversion methods are also adopted in transforming the prestack data from the offset domain to the ray-parameter domain, mixed-phase wavelet estimation, as well as the registration of P-P and P-S waves for the joint analysis. The ray impedance inversion methods are successfully applied to different types of datasets. In each individual step to achieving the ray impedance inversion, advantages, disadvantages as well as limitations of the algorithms adopted are detailed. As a conclusion, the ray impedance related analyses demonstrated in this thesis are highly competent compared with the classical elastic impedance methods and the author would like to recommend it for a wider application

Cumulant based identification approaches for nonminimum phase FIR systems

Cataloged from PDF version of article.In this paper, recursive and least squares methods for identification of nonminimum phase linear time-invariant (NMP-LTI) FIR systems are developed. The methods utilize the second- and third-order cumulants of the output of the FIR system whose input is an independent, identically distributed (i.i.d.) non-Gaussian process. Since knowledge of the system order is of utmost importance to many system identification algorithms, new procedures for determining the order of an FIR system using only the output cumulants are also presented. To illustrate the effectiveness of our methods, various simulation examples are presented

Recursive Detection of M-Ary Signals over Fast Varying Mobile Communication Channel

          Mobile radio is characterized by a fast time varying channel. Conventional detectors which designed optimal for non-fading channel exhibit a limited performance in fast time varying channel. In this paper a recursive detector for M-ary signals over fast time varying mobile communication channel is introduced. The proposed detector continuously estimates the channel directly within the metric calculation of the log-likelihood function in a recursive manner. The estimation of the channel is performed by the covariance form of the recursive least square approach. The performance of the detector is evaluated in terms of the misdetection probability. The effects of timing and phase offsets on the performance of detector are examined by simulation. Simulation results show that the proposed detector can accommodate the fast time varying channel with adequate performance