Leon H. Sibul

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In this paper, we present a method of parameter estimation for a class of problems where the desired signal is embedded in colored noise with unknown covariance. The new algorithm is a variation of the covariance differencing scheme proposed by Paulraj and Kailath. Unlike the previous method, however, the proposed algorithm does not require multiple(More)
Many acoustical applications require the analysis of a signal that is corrupted by an unknown filtering function. Examples arise in the areas of noise or vibration control, room acoustics, structural vibration analysis, and speech processing. Here, the observed signal can be modeled as the convolution of the desired signal with an unknown system impulse(More)
A stochastic fixed-point theorem is used as a basis for the study of stochastic convergence properties (in mean-squares sense) of the adaptive gradient lattice filter. Such properties include conditions on the stepsize in the adaptive algorithm and analytic expressions for the misadjustment and convergence rate. Our results indicate that the limits on the(More)
Classical results from group representation theory are used to gain insight into important properties of narrowband and wideband ambiguiry functions and wavelet transforms. Wideband ambiguity functions arc essentially affine wavelet transforms and narrowband ambiguity functions can be considered to be Heisenberg wavelet transforms. Important invariance(More)