Focused on the issue of anomaly detection for Unmanned Aerial Vehicle （UAV） flight data in the field of UAV airborne health monitoring， firstly， the characteristics of UAV flight data， the common flight data anomaly types and the corresponding demands on anomaly detection algorithms for UAV flight data were presented. Then， the existing research on UAV flight data anomaly detection algorithms was reviewed， and these algorithms were classified into three categories： prior-knowledge based algorithms for qualitative anomaly detection， model-based algorithms for quantitative anomaly detection， and data-driven anomaly detection algorithms. At the same time， the application scenarios， advantages and disadvantages of the above algorithms were analyzed. Finally， the current problems and challenges of UAV anomaly detection algorithms were summarized， and key development directions of the field of UAV anomaly detection were prospected， thereby providing reference ideas for future research.