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
an unbiased source location estimator. Alternative existing
estimators, including likelihood-based,
spherical interpolation, and quadratic-correction least-squares
estimators, are investigated and comparisons of their complexity,
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.
