The rise of biomimetic unmanned aerial vehicle clusters has brought new challenges to radar target tracking and recognition. The purpose of flying imitating geese flocks is long-distance transport with low energy consumption. In this paper, the methods of recognition and tracking are proposed for cluster targets with flight pattern of geese. The pattern recognition method, by detecting specific distance pairs according to five constraints including distance, vertical, pedal, orientation, and slope, judges whether there is a ’V’ characteristic in the measurement set and selects the key individuals such as the leading or following geese. Based on the pattern recognition result, the hypothetical pattern measurements are introduced into the filtering process as additional observation information, which aims to solve the problem of tracking accuracy decrease caused by high maneuverability in the formation stage of flying geese. Simulation results show that the proposed methods can not only judge the pattern of the cluster effectively and identify the key individuals but also improve the accuracy compared with the traditional tracking algorithms.

To obtain the accurate time difference of arrival (TDOA) and frequency difference of arrival (FDOA) for passive localization in an unmanned aerial vehicle (UAV) swarm, UAV swarm network synchronization is necessary. However, most of the traditional distributed time synchronization protocols are based on iteration, which hinders efficiency improvement. High communication costs and long convergence times are often required in large-scale UAV swarm networks. This paper presents a neighborhood selection-all selection (NS-AS) synchronization mechanism-based moving source localization method for UAV swarms. First, the NS-AS synchronization mechanism is introduced, to model the UAV swarm network synchronization process. Specifically, the UAV neighbors are first grouped by sector, and the most representative neighbors are selected from each sector for the state update calculation. When the UAV swarm network reaches a fully connected state, the synchronization mechanism is switched to select all neighbors, to improve the convergence speed. Then, the TDOA-FDOA joint localization algorithm is employed to locate the moving radiation source. Through simulation, the effectiveness of the proposed method is verified by the system convergence and localization performance under different parameters. Experimental results show that the synchronization mechanism based on NS-AS effectively improves the convergence speed of the system while ensuring the accuracy of moving radiation source localization.