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IPILT–OHPL: An Over-the-Horizon Propagation Loss Prediction Model Established by Incorporating Prior Information Into the LSTM–Transformer Structure

Authors :
Hanjie Ji
Lixin Guo
Jinpeng Zhang
Yiwen Wei
Xiangming Guo
Yusheng Zhang
Tianhang Nie
Jie Feng
Source :
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 10067-10082 (2024)
Publication Year :
2024
Publisher :
IEEE, 2024.

Abstract

The evaporation duct is a prevalent atmospheric structure in the marine lower troposphere with significant regional nonuniformity. This structure can trap radio waves inside its layer and enable them to propagate over the horizon with less loss. Therefore, accurately predicting the over-the-horizon propagation loss (OHPL) is important for optimizing the performance of radio-electronic systems. Considering the OHPL characteristics in nonuniform evaporation ducts, this article establishes an OHPL prediction model by incorporating prior information into the long short-term memory (LSTM)–transformer structure (IPILT–OHPL). The combination of the LSTM network and transformer is used to construct the LSTM–transformer, aimed at leveraging their respective strengths to extract important features of OHPL effectively. In addition, to improve the prediction accuracy, this article incorporates the evaporation duct height as an environmental prior information into the LSTM–transformer. Finally, this article comprehensively evaluates IPILT–OHPL model performance in different application scenarios. The evaluation results show that the established model not only has high prediction accuracy but also strong generalization ability, which provides a new method for efficiently predicting the OHPL in nonuniform evaporation ducts.

Details

Language :
English
ISSN :
19391404 and 21511535
Volume :
17
Database :
Directory of Open Access Journals
Journal :
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.28a1d336afb4dc49e51c25163e8c4bd
Document Type :
article
Full Text :
https://doi.org/10.1109/JSTARS.2024.3395630