@inproceedings{53f471a095f64a1db11c8e46d5e78781,
title = "Multipath amplitude estimation based on Bayesian inference in a non-Gaussian environment",
abstract = "Multipath amplitude estimation is addressed in this paper. However the received data is a mixture of real data and ocean noise which is complicated and is usually no longer follow Gaussian distribution or Laplacian distribution. To address this problem, an iterative algorithm based on Bayesian inference (BI) was proposed to estimate multipath amplitude with a zero-mean Gaussian mixture noise model (GMM). Numeric simulations illustrate the robustness and accuracy of the algorithm.",
keywords = "Bayesian inference, Gaussian mixture noise model, Multipath amplitude estimation",
author = "Ying Zhang and Kunde Yang and Zhixiong Lei",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018 ; Conference date: 28-05-2018 Through 31-05-2018",
year = "2018",
month = dec,
day = "4",
doi = "10.1109/OCEANSKOBE.2018.8559170",
language = "英语",
series = "2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018",
}