Efficient Neural Network Design for Radar Signal Recognition

In this paper, we propose an innovative approach to the problem of radar signal recognition, which is based on Neural Architecture Search (NAS). With the development of radar technology, radar signals become more and more diverse and complex, which brings great challenges to the traditional signal processing methods. Therefore, this paper aims to explore a more efficient and intelligent radar signal recognition scheme. The core contribution of this paper is the introduction of NAS technology to radar signal recognition. Unlike traditional deep learning-based methods that require manual design of the network structure, our approach automatically finds the optimal neural network structure through a pre-defined search space and search strategy. This method not only greatly reduces the time and effort required by the researchers to design the network structure, but also guarantees the performance superiority of the network model. Specifically, by automating the search process, we can find network architectures that are better suited to handling specific types of radar signals, thus improving the accuracy and efficiency of identification. To assess the validity of our proposed method, we undertook extensive experimentation across various standard datasets. The results of these experiments indicate that, in comparison to several existing sophisticated methods, our method has enhanced recognition accuracy, particularly in the handling of intricate and variable radar signals.