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

Ji, Hanjie; Guo, Lixin; Zhang, Jinpeng; Wei, Yiwen; Guo, Xiangming; Zhang, Yusheng; Nie, Tianhang; Feng, Jie

doi:10.1109/jstars.2024.3395630

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

2024

DOI: 10.1109/jstars.2024.3395630

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

Hanjie Ji,

Lixin Guo,

Jinpeng Zhang

et al.

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Cited by 2 publications

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EKDInformer-LTEDH: An Informer-Based Environmental Knowledge-Driven Prediction Model for Long-Term Evaporation Duct Height

Ji,

Wei,

Zhao

et al. 2024

IEEE Geosci. Remote Sensing Lett.

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EKDInformer-LTEDH: An Informer-Based Environmental Knowledge-Driven Prediction Model for Long-Term Evaporation Duct Height

Ji,

Wei,

Zhao

et al. 2024

IEEE Geosci. Remote Sensing Lett.

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EDH-STNet: An Evaporation Duct Height Spatiotemporal Prediction Model Based on Swin-Unet Integrating Multiple Environmental Information Sources

Ji,

Guo,

Zhang

et al. 2024

Remote Sensing

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Given the significant spatial non-uniformity of marine evaporation ducts, accurately predicting the regional distribution of evaporation duct height (EDH) is crucial for ensuring the stable operation of radio systems. While machine-learning-based EDH prediction models have been extensively developed, they fail to provide the EDH distribution over large-scale regions in practical applications. To address this limitation, we have developed a novel spatiotemporal prediction model for EDH that integrates multiple environmental information sources, termed the EDH Spatiotemporal Network (EDH-STNet). This model is based on the Swin-Unet architecture, employing an Encoder–Decoder framework that utilizes consecutive Swin-Transformers. This design effectively captures complex spatial correlations and temporal characteristics. The EDH-STNet model also incorporates nonlinear relationships between various hydrometeorological parameters (HMPs) and EDH. In contrast to existing models, it introduces multiple HMPs to enhance these relationships. By adopting a data-driven approach that integrates these HMPs as prior information, the accuracy and reliability of spatiotemporal predictions are significantly improved. Comprehensive testing and evaluation demonstrate that the EDH-STNet model, which merges an advanced deep learning algorithm with multiple HMPs, yields accurate predictions of EDH for both immediate and future timeframes. This development offers a novel solution to ensure the stable operation of radio systems.

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

Cited by 2 publications

References 40 publications

EKDInformer-LTEDH: An Informer-Based Environmental Knowledge-Driven Prediction Model for Long-Term Evaporation Duct Height

EKDInformer-LTEDH: An Informer-Based Environmental Knowledge-Driven Prediction Model for Long-Term Evaporation Duct Height

EDH-STNet: An Evaporation Duct Height Spatiotemporal Prediction Model Based on Swin-Unet Integrating Multiple Environmental Information Sources

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