Fair and Andrews´s weighting-based IRWLS algorithms for time-delay estimation in underwater target tracking

Underwater target tracking relies on a model relating the target states to time-delay and bearing measurements. This furnishes linearized measurement models. Problems arise due to fitting models using the least-squares procedure, whose success may depend on the assumption that the data noise distribution is Gaussian. For many cases of non-Gaussian errors, performance of the least-squares estimators is far from optimal. Robust regression procedures have been proposed to improve the performance of the least-squares procedures for non-Gaussian errors, and to enhance their performance for the Gaussian errors. Filters for time-delay estimation based on the Fair and Andrews´s weighting functions of the iteratively reweighted least-squares method are proposed. Computational results are given to illustrate and compare the performances of the two filters as well as that due to ordinary least-squares filters