By using the self-developed unmanned aerial vehicle （UAV） borne mini synthetic aperture radar （MiniSAR） system， all-directional echoes for multiple types of representative ground targets are collected and used for image processing by the Radar Detection and Imaging Techniques Research Group of Nanjing University of Aeronautics & Astronantics（NUAA）. A proprietary SAR database for complex targets is constructed， based on which artificial-intelligence-inspired target recognition approaches are studied. To solve the challenging issue of SAR image defocus caused by unstable movement of the UAV platform and the limited accuracy provided by the accessory sensors， novel movement compensation and auto-focus processing methods are proposed. The impact of the image defocus on the classification accuracies provided by the neural networks is revealed， and the limited generalizability of the existing neural networks is demonstrated. Simulation results show that although the classic neural networks， such as AlexNet， ResNet-18， AConvNet， and VGG offer a near-100% accuracy for the MSTAR 10-target classification problem， the 9-class target classification accuracies provided by these networks for the MiniSAR dataset are all much lower than 90%. Since the experiment method employed in this paper closely resembles the practical application scenario， the proposed database will be of great reference value to the development of SAR target recognition algorithms for engineering applications.