Analysis of the Applicability of Blind Beamforming Techniques using Deep Neural Network to Defense Systems

This paper compares the performance of deep neural networks(DNN) applied to antenna arrays with different element counts(8, 16, 32). The DNNs were designed using fully connected(FC) layers, comprising input, hidden, and output layers. Each hidden layer includes a single FC layer and a rectified linear unit(ReLU) activation function. Results indicate that blind beamforming with DNNs performs well with fewer elements but degrades as the number of elements increases due to increased nonlinearity, complicating training. State-of-the-art defense radar systems require many array elements, making current research insufficient. To effectively apply DNN-based blind beamforming to these large arrays, further research is needed to address the signal-to-interference noise ratio(SINR) performance degradation associated with larger array elements.