Aiming at the problems of beam training and target localization and tracking in millimeter-wave （mmWave） low-altitude unmanned aerial vehicle （UAV） scenarios， inspired by information theory， this paper proposes a hierarchical beam training algorithm based on the channel coding principle and a UAV target localization and tracking algorithm based on mmWave radar sensing， respectively. The proposed algorithms have high generalization and robustness， and are applicable not only to static and dynamic scenarios， but also to far-field， near-field， reconfigurable intelligent surface （RIS） assisted communication， and distributed cellular-free network scenarios， as well as illegal UAV intrusion sensing， etc. The algorithms are also validated through simulations and hardware platform tests. Specifically， the channel coding beam training algorithm can significantly improve the training accuracy by using coding gain and error correction mechanism. The mmWave radar algorithm combines Capon beam formation， constant false alarm rate （CFAR） detection and density-based spatial clustering of applications with noise （DBScan） to achieve UAV detection and tracking. Both simulation and hardware test results show that these algorithms can effectively improve the efficiency of beam training and the accuracy of sensing and localization in mmWave low-altitude UAV scenarios， providing technical support for the further prosperous development of low-altitude economy.