Deep Learning for Radar Target Recognition Based on IFL Algorithm

As a sub field of machine learning, deep learning has shown outstanding benefits in several areas compared to more traditional, non-deep machine learning. However, continuous expansion of learning cannot be achieved by relying solely on existing deep learning methods. Therefore, this paper proposes a deep learning-based IFL (instant feature learning) algorithm for radar target recognition. The Adaptive Continual Memory (ACM) algorithm is used as the main method, supplemented by HOG feature extraction algorithm and HNSW index optimization algorithm, to carry out initial segmentation and learning of the dataset, and to carry out continuous learning, training, and testing based on the data stream, to achieve the effect of instant feature learning. The experiments of the IFL algorithm for radar target recognition based on deep learning in the field of radar target recognition verified its breakthrough progress in instant feature learning, especially in the satellite target recognition task achieved 99.86% accuracy, fully proving the practical value of the algorithm.