Real-time passive source localization: A practical linear-correction least-squares approach

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 efficient source location estimator wi thout assuming a priori knowledge of noise distribution. Alternative existi ng estimators, including likelihood-based, spherical intersection, spherica l interpolation, and quadratic-correction least-squares estimators, are rev iewed and comparisons of their complexity, estimation consistency and effic iency against the Cramer-Rao lower bound are made. Numerical studies demons trate that the proposed estimator performs better under many practical situ ations.