📝 SummarXiv

Abstract

This paper provides the first comprehensive review of fifty years of synthetic aperture radar automatic target recognition (SAR ATR) development, tracing its evolution from inception to the present day. Central to our analysis is the inheritance and refinement of traditional methods, such as statistical modeling, scattering center analysis, and feature engineering, within modern deep learning frameworks. The survey clearly distinguishes long-standing challenges that have been substantially mitigated by deep learning from newly emerging obstacles. We synthesize recent advances in physics-guided deep learning and propose future directions toward more generalizable and physically-consistent SAR ATR. Additionally, we provide a systematically organized compilation of all publicly available SAR datasets, complete with direct links to support reproducibility and benchmarking. This work not only documents the technical evolution of the field but also offers practical resources and forward-looking insights for researchers and practitioners. A systematic summary of existing literature, code, and datasets are open-sourced at \href{https://github.com/JoyeZLearning/SAR-ATR-From-Beginning-to-Present}{https://github.com/JoyeZLearning/SAR-ATR-From-Beginning-to-Present}.