@inproceedings{65fbb5e768134722bf51d45c19867ec0,
title = "LMMSE-Aided WLLS Location Estimators for Source Localization with RSS Measurements",
abstract = "Received signal strength (RSS) measurements can be converted to the distance estimates between the emission source and the sensors to construct a system of linear equations, thereby allowing for the use of the weighted linear least squares (WLLS) estimators for location estimation. However, estimating the squared distances from the RSS measurements governed by the log-normal shadowing effect presents a major challenge in such approaches. In this paper, we propose a linear minimum mean square error (LMMSE) estimator of the squared distance between the emission source and the sensor first. Then a LMMSE-aided WLLS (LMMSE-WLLS) location estimator and its unbiased counterpart are presented for source localization. Furthermore, their estimation performance are analyzed in terms of mean square error (MSE) and covariance. It is found that the proposed LMMSE-aided WLLS location estimators have better estimation performance than existing WLLS estimators. Numerical examples also demonstrate the performance superiority of the proposed location estimators for source localization.",
keywords = "LMMSE, RSS, WLLS, source localization, wireless sensor networks",
author = "Donglin Zhang and Zhansheng Duan and Yiyong Sun and Feng Yin",
note = "Publisher Copyright: {\textcopyright} 2024 ISIF.; 27th International Conference on Information Fusion, FUSION 2024 ; Conference date: 07-07-2024 Through 11-07-2024",
year = "2024",
doi = "10.23919/FUSION59988.2024.10706329",
language = "英语",
series = "FUSION 2024 - 27th International Conference on Information Fusion",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "FUSION 2024 - 27th International Conference on Information Fusion",
}