Sequential source localisation and range estimation based on shrinkage algorithm

Abstract

This study presents the shrinkage-based sequential source localisation and range estimation algorithms. The shrinkage factor is found using the variance of the estimate in the existing shrinkage algorithm. However, the variance of the estimate is difficult to calculate when the form of the estimate is complex. To circumvent this problem, the authors propose a shrinkage algorithm that employs the Cramér-Rao lower bound (CRLB) instead of the variance for the maximum likelihood (ML) estimate. The variance of the ML estimate and CRLB were found to be similar in simulation results. Furthermore, Stein's unbiased risk estimator and Ledoit-Wolf methods are used to determine the shrinkage factor. The resulting estimation accuracy of the proposed shrinkage-based sequential source localisation and range estimation methods was similar with that of the existing shrinkage algorithm.

Shrinkage sinusoidal phase estimation based on spherical simplex unscented transform and bootstrap method

“…Shrinkage estimators have been utilized to improve the accuracy of the unbiased estimator in low SNR environments . The variance of the MLE or Cramér‐Rao lower bound (CRLB) is utilized for obtaining the shrinkage factor . The task of covariance matrix estimation is problematic when the number of samples is small compared with the number of variables.…”