Robust range estimation algorithm based on hyper-tangent loss functionopen access
- Authors
- Park, Chee-Hyun; Chang, Joon Hyuk
- Issue Date
- Jul-2020
- Publisher
- INST ENGINEERING TECHNOLOGY-IET
- Keywords
- statistical analysis; Monte Carlo methods; impulse noise; mean square error methods; signal processing; estimation theory; robust range estimation algorithm; hyper-tangent loss function; M estimator; robust signal processing; statistical testing; received signal magnitude; information theoretic learning; mean square error performance; mean square error performance; Monte Carlo simulation; MSE performance
- Citation
- IET SIGNAL PROCESSING, v.14, no.5, pp.314 - 321
- Indexed
- SCIE
SCOPUS
- Journal Title
- IET SIGNAL PROCESSING
- Volume
- 14
- Number
- 5
- Start Page
- 314
- End Page
- 321
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/9671
- DOI
- 10.1049/iet-spr.2019.0343
- ISSN
- 1751-9675
- Abstract
- Herein, the authors present a robust estimator of range against the impulsive noise using only the received signal's magnitude. TheMestimator has been widely used in robust signal processing. However, the existingMestimator 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 theMestimator based on the hyper-tangent loss function, which does not demand statistical testing. ConventionalMestimator 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.
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