An efficient linear-correction least-squares approach to source localization

A linear-correction least-squares estimation procedure is proposed for the source localization problem under an additive measurement error model. The method, which can be easily implemented in a real-time system with moderate computational complexity, yields a source location estimator with a small bias and a small variance. Alternative existing estimators, including likelihood-based, spherical interpolation, and quadratic-correction least-squares estimators, are investigated and comparisons of their estimation consistency and efficiency against the Cramer-Rao lower bound are made. Numerical studies demonstrate that the proposed estimator performs better under many practical situations.