Abstract
Kormylo and Mendel proposed a maximum-likelihood deconvolution (MLD) algorithm for estimating a desired sparse spike sequence μ(k), modelled as a Bernoulli-Gaussian (BG) sigbal, which was distorted by a linear time-invariant system v(k). Then Chi, Mendel and Hampson proposed another MLD algorithm which is a computationally fast MLD algorithm and has been successfully used to process real seismic data. In this paper, we propose an adaptive MLD algorithm, which allows v(k) to be a slowly time-varying linear system, for estimating the BG signal μ(k) from noisy data. Like the previous MLD algorithms, the proposed adaptive MLD algorithm can also recover the phase of v(k) when v(k) is time-invariant. Some simulation results are provided to support the proposed algorithm. © 1991.