期刊
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
卷 19, 期 9, 页码 5819-5832出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2020.2997455
关键词
Estimation; Kalman filters; Wireless communication; Computational complexity; Pollution measurement; Probability density function; Standards; Localization; Robust; maximum likelihood-type estimator (M estimator); multi-stage maximum likelihood-type estimator (MM estimator); extrapolated single propagation unscented Kalman filter; weighted least squares
资金
- Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant - Korean Government (MSIT), Intelligent Signal Processing for AI Speaker Voice Guardian [2017-0-00474]
- National Research Foundation of Korea (NRF) - Korean Government (MSIT) [NRF-2017R1D1A1B03032895]
- Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [2017-0-00474-004] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This paper presents robust localization algorithms that use range measurements to estimate the location parameters. The non-line-of-sight (NLOS) propagation of a signal can severely deteriorate the estimation performance in indoor and population-dense urban areas. Therefore, the robust localization algorithms are considered in this paper. In particular, the robust statistics-based localization is dealt with. The maximum likelihood (ML)-type and multi-stage ML-type method-based weighted least squares (WLS) algorithms and robust extrapolated single propagation unscented Kalman filter (ESPUKF) are proposed for mixed line-of-sight (LOS)/NLOS environments. Based on extensive simulations, the positioning accuracies of the proposed methods are found to be superior to those of conventional methods in the mildly and moderately mixed LOS/NLOS conditions. In addition, analyses are conducted on the mean square error (MSE), asymptotical unbiasedness and computational complexity of the proposed algorithms.
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