Robust Anti-Jamming Method for Large-Array Radar Systems Using Deep Learning Based Null-Space Beamforming

With the increasing use of AI in radar signal processing, researchers have started combining minimum variance distortionless response (MVDR) with AI technology; however, the use of MVDR results in higher nonlinearity, making the learning process difficult. A combination of null-space beamforming (NSB) and AI technology may be a solution to this issue. However, inferring NSB weights requires inputting prior angle information, which necessitates an additional direction of arrival estimation, thereby increasing system complexity. To meet the requirements for applications in radar systems, we present a robust anti-jamming method for large-array radar systems using a deep neural network with NSB (DNN-NSB). The proposed method combines the computational simplicity of null-space beamforming with the adaptability of deep learning to effectively suppress interference and maintain a high signal-to-interference-plus-noise ratio (SINR). Unlike traditional methods, DNN-NSB eliminates the need for prior angle information, enabling efficient and scalable weight inference even in complex scenarios. The performance of DNN-NSB was validated through simulations across four scenarios by varying the number of interference sources, interference-to-signal ratio (ISR) conditions, and array sizes. The results showed that DNN-NSB consistently achieved near-optimum SINR within the training range and demonstrated superior performance compared to a convolutional neural network based on MVDR (CNN-MVDR) under multi-source interference conditions. To evaluate scalability, the model was further tested using a 32-element array, where it consistently achieved near-optimum interference suppression and maintained high spatial resolution. In conclusion, the study findings highlight the potential of DNN-NSB as a practical and effective solution for modern radar systems, particularly for applications that require large arrays and robust anti-jamming capabilities.