A data driven approach for detection and isolation of anomalies in a group of UAVs

作者：Yin Wang^*; Wang Daobo; Wang Jianhong

来源：Chinese Journal of Aeronautics, 2015, 28(1): 206-213.

DOI：10.1016/j.cja.2014.12.003

• Combinatorial optimization
• Fault detection
• Gradient methods
• Model identification
• Unmanned aerial vehicle

The use of groups of unmanned aerial vehicles (UAVs) has greatly expanded UAV's capabilities in a variety of applications, such as surveillance, searching and mapping. As the UAVs are operated as a team, it is important to detect and isolate the occurrence of anomalous aircraft in order to avoid collisions and other risks that would affect the safety of the team. In this paper, we present a data-driven approach to detect and isolate abnormal aircraft within a team of formatted flying aerial vehicles, which removes the requirements for the prior knowledge of the underlying dynamic model in conventional model-based fault detection algorithms. Based on the assumption that normal behaviored UAVs should share similar (dynamic) model parameters, we propose to firstly identify the model parameters for each aircraft of the team based on a sequence of input and output data pairs, and this is achieved by a novel sparse optimization technique. The fault states of the UAVs would be detected and isolated in the second step by identifying the change of model parameters. Simulation results have demonstrated the efficiency and flexibility of the proposed approach.

• 出版日期2015-2
• 单位中国电子科技集团公司第二十八研究所; 南京航空航天大学