Radar Automatic Target Recognition （RATR） has widespread applications in both domains of military and civilian. Due to the robustness caused by that ensemble learning improves model classification performance by integrating the existing machine learning models， ensemble learning has been applied in the field of radar target detection and recognition increasingly. The research progress of ensemble learning in RATR was discussed in detail through systematic sorting and refining the existing relevant literature. Firstly， the concept， framework， and development process of ensemble learning were introduced， ensemble learning was compared with traditional machine learning and deep learning methods， and the advantages， limitations， and main focuses of research of ensemble learning theory and common ensemble learning methods were summarized. Secondly， the concept of RATR was described briefly. Thirdly， the applications of ensemble learning in different radar image classification features were focused on， with a detailed discussion on target detection and recognition methods based on Synthetic Aperture Radar （SAR） and High-Resolution Range Profile （HRRP）， and the research progress and application effect of these methods were summed up. Finally， the challenges faced by RATR and ensemble learning were discussed， and the applications of ensemble learning in the field of radar target recognition were prospected.