Shrinkage estimation-based source localization with minimum mean squared error criterion and minimum bias criterion

In this paper, we propose two novel source localization methods; one is the shrinkage estimator with the minimum mean squared error criterion, and the other is the shrinkage estimator with the minimum bias criterion. The mean squared error performance of the two-step weighted least squares deteriorates in the large noise variance regimes. In order to improve the two-step weighted least squares in the large noise variance regimes, the shrinkage factor is multiplied by the two-step weighted least squares estimator, and then the novel estimator is determined such that the mean squared error and squared bias are minimized. Simulation results show that the mean squared error performances of the proposed methods are better than those of the two-step weighted least squares method as well as the minimax estimator in a regime with large measurement noise variances.