A fast maximum-likelihood estimation and detection algorithm for Bernoulli-Gaussian processes

We derive and implement a maximum-likelihood detection and estimation algorithm based on the same channel and statistical models used by Kormylo and Mendel [1], that leads to less computations than the approach presented by Chi, Mendel and Hampson [2]. We introduce a single generalized likelihood function and we develop the Multiple-Most-Likely Replacement (MMLR) detector. This detector is computationally faster compared with the Single-Most-Likely Replacement (SMLR) detector developed by Kormylo and Mendel [3]. We demonstrate good performance of our algorithm for a synthetic data example.