Abstract
The authors derive and implement a maximum-likelihood detection and estimation algorithm, based on the same channel and statistical models used by J. Kormylo and J. M. Mendel (1983) that leads to less computations than the approach presented by C. Y. Chi, et al. (1984). They introduce a single generalized likelihood function and develop the multiple-most-likely replacement (MMLR) detector. This detector is computationally faster than the single-most-likely replacement (SMLR) detector developed by J. Kormylo and J. M. Mendel (1982). They demonstrate good performance of their algorithm for a synthetic data example.