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Herein, we present robust shrinkage range estimation algorithms for which received signal strength measurements are used to estimate the distance between emitter and sensor. The concepts of robustness for the Hampel filter and skipped filter are combined with shrinkage for the positive blind minimax and Bayes shrinkage estimation. It is demonstrated that the estimation accuracies of the proposed methods are higher than those of the existing median-based shrinkage methods through extensive simulations.

This paper presents novel shrinkage‐based sinusoidal phase estimation algorithms. The main contributions of this paper are two‐fold. First, the shrinkage factor is found using the spherical simplex unscented transform (SSUT) and the combination of bootstrap and SSUT to reduce the computational complexity of the Monte Carlo method. The computational burden of the proposed methods is relatively low due to the use of SSUT, resulting from the carefully selected sigma points. Second, the accuracy of the novel shrinkage estimator is better than that of the maximum likelihood estimator (MLE) and existing shrinkage estimator under the low signal‐to‐noise ratio (SNR) and small sample number conditions. It is demonstrated that the resulting accuracies of the proposed shrinkage‐based phase estimation methods are superior to those of existing shrinkage algorithm and MLE in low SNR and small sample number conditions.