To solve the problem that a parameter search easily falls into a local optimum and the two-dimensional electron density profile construction error is large in the process of backscatter ionogram inversion, an improved method using neighborhood-aided and multistep fitting is proposed. The ionospheric parameter inversion results in the adjacent space are combined and reconstructed by using the neighborhood-aided correction method. The introduction of auxiliary information sources addresses the defects of the conventional genetic algorithm. The local region multistep fitting method is used to describe the local uniformity and global inhomogeneity of the two-dimensional electron density profile by dividing the fitting region. The experimental results show that the proposed method can improve the accuracy of backscatter ionogram inversion and provide reliable support for tracking radio ray trajectories.
Since solving the path of radio wave propagation by an analytic method in the process of coordinate registration (CR) for skywave over-the-horizon radar (OTHR) is difficult, a reference source-aided CR method that uses ensemble learning is proposed. First, the OTHR wave propagation principle is briefly described, and the ionospheric channel characteristics implied by reference sources and target information are explained, which provides a theoretical basis for modelling. Then, multiple machine learning models, such as random forest, support vector machine and elastic network, are used to build a mapping network between known information and the actual ground distance of the targets. Moreover, the prediction ability and correlation of each machine learning model are evaluated. Finally, with stacking ensemble learning, machine learning models with better performance to solve CR problems are effectively combined as base learners. The models develop the advantages of each base learner to achieve a perception of the ionospheric environment and accurate OTHR CR. The simulation results show that the proposed method can mine the ionospheric channel propagation characteristics and effectively reduce the CR error of the targets of interest. When the information provided by 4 reference sources 200 km away from the target is used, the average error of target positioning is 13 km.
K E Y W O R D S coordinate registration, electromagnetic environment cognition, ensemble learning, over-the-horizon radarThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
For skywave over-the-horizon radar, beamforming techniques are often used to suppress airspace radio frequency interference because the high-frequency band is shared by many devices. To address the problems that the traditional beamforming method is not capable of recognizing the electromagnetic environment and that its performance is greatly affected by the accuracy of signal feature estimation, a cognitive beamforming method using range-Doppler (RD) map features for skywave radar is proposed. First, the RD map is weighted by a local attention model, and then, texture features are extracted as the inputs to a support vector machine. Finally, the support vector machine is used to predict the optimal diagonal loading factor. Simulation results show that the output signal-to-interference-plus-noise ratio is improved compared with previous methods. The proposed method is suitable for many kinds of common unsatisfactory scenarios, making it beneficial for engineering implementation.
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