Recursive SMLR deconvolution algorithm for Bernoulli-Gaussian signals

In the past decade, many detection and estimation algorithms have been reported for estimating a desired Bernoulli-Gaussian signal which was distorted by a linear time-invariant system. The well known Kormylo and Mendel´s (1982) single most likely replacement (SMLR) algorithm, which works well and has been successfully used to process real seismic data, is an offline signal processing algorithm. The paper proposes a recursive SMLR algorithm which has online data processing capabilities and requires much less computational effort than Chi and Mendel´s (1984) recursive algorithm and Goussard and Demoment´s (1989) recursive algorithm. Simulation results show good performance