The radar detection and imaging team at Nanjing University of Aeronautics and Astronautics （NUAA） has developed a circular synthetic aperture radar （SAR） dataset for over ten types of typical military targets， including tanks， armored vehicles， and fighter aircraft， using a self-developed UAV MiniSAR system. Through high-precision imaging processing of multiple field trial datasets， the team expands the original monostatic circular SAR image dataset with multiple elevation angles to include bistatic circular SAR image datasets with varying bistatic angle configurations. Building upon this dataset and integrating the team’s research achievements in SAR image target detection， recognition methodologies， and applications， this paper reviews and summarizes deep learning-based SAR target detection and recognition techniques， comparing the performance of different neural network models on NUAA’s MiniSAR dataset. Specifically， for target detection， the paper proposes to leverage the inherent attributes of SAR images to extract target location information and incorporates a lightweight one-stage detection algorithm. Additionally， an algorithm utilizing information distribution patterns combined with a global attention mechanism is introduced to enhance the detection accuracy and efficiency of small targets in complex backgrounds. For target recognition， after suppressing interference noise using SAR image prior information， the study presents a recognition algorithm that integrates multi-view SAR target information with Transformer architecture. Furthermore， a viewpoint regularization term is designed to constrain interview correlations， which enables effective feature fusion across different perspectives to improve the recognition accuracy of small SAR targets. Addressing the practical requirement of real-time ground target detection and recognition by MiniSAR systems， this paper also explores the deployment strategy of lightweight detection and recognition networks on digital signal processing （DSP） platforms， accompanied by preliminary experimental results. Finally， the challenges and future trends in intelligent SAR target detection and recognition are discussed.