Skip to Main Content
We derive and implement a maximum-likelihood detection and estimation algorithm based on the same channel and statistical models used by Kormylo and Mendel , that leads to less computations than the approach presented by Chi, Mendel and Hampson . 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 . We demonstrate good performance of our algorithm for a synthetic data example.