Atmospheric duct propagation has a significant influence on the performance of wireless communication and radar systems in the maritime environment that makes it essential to master the detailed knowledge of the atmospheric refractivity profile. Automatic identification system (AIS) is a maritime navigation safety communication system that operates in the very high frequency mobile band and the propagation path of AIS signals will be influenced by different atmospheric conditions. In this paper, a new refractivity estimation method based on the AIS signal level is proposed and the Levy flight quantum-behaved particle swarm optimization (LFQPSO) algorithm is presented and applied in the new estimation method to estimate the surface based duct parameters. Numerical simulations demonstrate that the LFQPSO algorithmhas good robustness and the new refractivity estimation method based on the AIS signal level can provide near-real-time estimation ofatmospheric refractivity.

Electromagnetic wave propagates abnormally under atmospheric duct conditions. It is very important to study the influence of atmospheric duct on the radio propagation of the shipborne automatic identification system (AIS). AIS is a maritime navigation safety communication system that operates in the very high frequency (VHF) maritime mobile band and was developed primarily for collision avoidance. When AIS signals propagating in the atmospheric duct, the trapping effect can extend the signal range beyond the line-of-sight. In this work, the transmission of AIS signals under atmospheric duct conditions is modelled using the parabolic equation method. The simulation results demonstrate that both standard surface duct and surface-based duct can extend the detection range of AIS, whereas surface-based duct shows higher trapping capabilities over the standard surface duct. The results can be implemented to improve maritime situation awareness.

Automatic identification system (AIS) is a maritime navigation safety communication system that operates in the very high frequency (VHF) mobile band and was developed primarily for collision avoidance. Since the height of the antenna cannot be extended indefinitely, communication distance of the AIS is usually within the horizon. However, with the increasing importance of the AIS traffic, this line-of-sight propagation mechanism cannot meet the need to monitor shipping at a longer distance. When AIS signal is propagating in the atmospheric duct, the trapping effect allows it to propagate over-the-horizon. In this work, the parabolic equation method is utilized to calculate the propagation loss of AIS signals in ducting channel and the simulation results show that AIS can perform over-the-horizon propagation. In order to obtain the distribution of atmospheric refractivity profile over the sea surface, a new inversion method based on the AIS signal level is proposed. The particle swarm optimization (PSO) algorithm is selected to minimize the objective function for obtaining the atmospheric refractivity profile in the maritime environment. Numerical simulations are presented to validate this method for refractivity profile estimation, which provides a theoretical basis and effective support for the applications of over-the-horizon communication and radar over-the-horizon detection.

In order to verify the feasibility of atmospheric duct inversion method using automatic identification system signal in the actual sea area, a sea experiment was carried out. The signal acquisition system of portable high sensitivity civil automatic identification system is developed, and the receiving and processing process of automatic identification system signal is analysed. The sea experiment scheme and main experiment equipment are given, and the collected experiment data are processed and analysed. Finally, the method of atmospheric duct inversion using automatic identification system signal is verified by the measured data. Atmospheric duct inversion is an inverse problem, which requires the use of various optimisation algorithms to optimise the duct parameters in order to obtain the characteristic parameter information of the duct. In this paper, the Lévy flight quantum‐behaved particle swarm optimisation algorithm and the deep learning algorithm are respectively used to invert the atmospheric duct parameters. The results show that there is little difference between the inverted duct parameters and the true duct parameters, which verifies the feasibility of using the automatic identification system signal to invert the atmospheric duct.

To address the insufficient retrieval accuracy of temperature and humidity profiles from ground-based microwave radiometers, this study develops an improved retrieval method based on intelligent optimization and attention mechanisms under clear-sky conditions. The research first constructs a brightness temperature dataset by performing forward modeling of ERA5 reanalysis data for the Haikou region using the MonoRTM model. Subsequently, five common machine learning algorithms—Ridge Regression, XGBoost, Multilayer Perceptron (MLP), Random Forest, and Support Vector Machine (SVM)—are employed to reconstruct atmospheric temperature and humidity profiles. A systematic evaluation of the retrieval results reveals distinct performance characteristics across the five machine learning models. For temperature retrieval, the Wolf Pack Algorithm (WPA) is introduced to optimize SVM parameters, resulting in a WPA-SVM model that reduces the overall Root Mean Square Error (RMSE) by 13.2% compared to conventional SVM. For humidity retrieval, an innovative MLP model integrated with an attention mechanism is proposed. By incorporating adaptive weighting and oversampling strategies, this model significantly improves retrieval accuracy at high altitudes, achieving a 15.1% reduction in overall RMSE compared to traditional MLP. The hybrid retrieval framework developed in this study integrates swarm intelligence and machine learning, under clear-sky conditions, providing a reliable technical pathway for high-precision and high-robustness atmospheric parameter retrieval. These findings not only hold significant value for advancing high-accuracy atmospheric parameter remote sensing systems, but also establish a foundation for precisely retrieving atmospheric refractivity profiles to resolve atmospheric ducts, thereby carrying important implications for marine wireless communications.