Robust range estimation algorithm based on hyper-tangent loss function

Abstract

Herein, the authors present a robust estimator of range against the impulsive noise using only the received signal's magnitude. The M estimator has been widely used in robust signal processing. However, the existing M estimator requires statistical testing involving a threshold which has an optimality that varies with time, hence algorithmically challenging and computationally burdensome. The statistical testing is utilised for discerning the inlier and outlier. Further, statistical testing renders the computational burden of the algorithm high since the testing must be performed for each observation. Therefore, they propose the M estimator based on the hyper-tangent loss function, which does not demand statistical testing. Conventional M estimator employing information theoretic learning also does not call for statistical testing, but the mean square error (MSE) performance for the range estimation is inferior to that of the proposed method. Furthermore, they perform an analysis for the MSE for the proposed algorithm. Monte Carlo simulations not only validate their theoretical analysis, but also demonstrate the MSE performance of the proposed method is nearly same as the existing skipped filter although it does not require the statistical testing and optimal threshold selection.