Author： Kaufhold B. Kirlin R.L. Dizaji R.M.
Publisher： Academic Press
ISSN： 1051-2004
Source： Digital Signal Processing, Vol.9, Iss.1, 1999-01, pp. : 18-35
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Abstract
A method for the blind identification of spatially varying transfer functions found in various remote sensing applications such as medical imagery, radar, sonar, and seismology is described. The techniques proposed herein are based on model matching of Fourier coefficient sensitivity vectors of a known transfer function, which can be nonlinear in the parameters, with a set of eigenvectors obtained from data covariance matrices. One distinction between this technique and usual channel subspace methods is that no FIR structure for the individual transfer functions is assumed. Instead we assume that the frequency response as a function of the parameters is known as is often the case in wave transmission problems. A channel identification procedure based on subspace matching is proposed. The procedure matches the eigenvectors of the signal deviation covariance matrix to a set of scaled and energy-normalized sensitivity vectors. For the case where neither the number of channels, the model parameters of each channel nor the membership assignment of data traces to the channels is known, we propose a novel preliminary clustering process. By separating the data into clusters of modest variability such that the measurements are linear with the parameters, we are able to deduce all of the above. The clustering is based on feature vectors obtained from a time-frequency entropy measure, also a novelty of our paper. To support the theory developed, we include parameter estimation results based on simulated data backscattered from a synthetic multi-layer structure.
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