Description:
For skywave over-the-horizon radar, beamforming techniques are often used to suppress airspace radio-frequency interference (RFI) 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 via range-Dopple (RD) map features (RDF-CB) for skywave radar is proposed. First, the RD map is weighted by a local attention model, and then, texture features are extracted as the input to a support vector machine (SVM). Finally, an SVM is used to predict the optimal diagonal loading factor. Simulation results show that the output signal-to-interference-plus-noise ratio (SINR) is improved compared with previous methods. The proposed method is suitable for many kinds of common unsatisfactory scenarios, making it beneficial for engineering implementation