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Robust Localization Employing Weighted Least Squares Method Based on MM Estimator and Kalman Filter With Maximum Versoria Criterion
- Authors
- Park, Chee-Hyun; Chang, Joon-Hyuk
- Issue Date
- 2021
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Kalman filters; Location awareness; Signal processing algorithms; Sensors; Indexes; Prediction algorithms; Covariance matrices; Impulsive noise; kalman filter; localization; MM estimator; robust; versoria function
- Citation
- IEEE SIGNAL PROCESSING LETTERS, v.28, pp.1075 - 1079
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE SIGNAL PROCESSING LETTERS
- Volume
- 28
- Start Page
- 1075
- End Page
- 1079
- ISSN
- 1070-9908
- Abstract
- This study presents a robust two-step weighted least squares (WLS) localization algorithm using the MM estimator and the Kalman filter with the maximum Versoria criterion (MVC). An outlier-resistant statistic for the actual transformed distance is determined and the covariance matrix of the outlier-resistant statistic is calculated. This covariance matrix is used in the two-step WLS method. The simulation results demonstrate that the localization performances of the proposed algorithms outperform that of the conventional methods.
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Related Researcher
- Chang, Joon-Hyuk
- COLLEGE OF ENGINEERING (SCHOOL OF ELECTRONIC ENGINEERING)