Alternate amplitude weighting approach for passive source localization using the energy-based grid search algorithm

This paper focuses on amplitude weight calculation and application for near-field passive source localization utilizing an energy-based grid search algorithm. The main contribution is the presentation of a suboptimal weighting estimator. It is evaluated and compared with the optimal weighting estimator using Monte Carlo simulation. It is also compared with the Cramer-Rao bound (CRB), a theoretical lower performance bound. Both the optimal and suboptimal estimators bring obvious performance improvement over the original source localization algorithm and show a close correspondence with the CRB for either colored or white Gaussian noise cases. Since the computational load of the optimal estimator is higher than the suboptimal one, it is clear that the suboptimal estimator is more attractive for practical implementation.