A Multi-Sources DOA Localization Method Based on UAV Cluster Systems

Multi-sources Direction of Arrival (DOA) localization by Unmanned-Aerial-Vehicle (UAV) cluster systems has gained significant attention due to its flexibility and high precision. The key technology lies in high-resolution multi-sources DOA estimation. UAV cluster systems with traditional element-space structure often suffer the extreme demands of Radio Frequency (RF) chains, resulting in high hardware complexity and computation burden. Additionally, the unknown phase differences among all the UAV units caused by asynchronous crystal oscillators and imprecise unit locations further complicates this problem. To address these issues, a beamspace ESPRIT based multi-sources DOA localization method is proposed in this paper. The beamspace pre-processing with the hybrid structure is used to handle the RF chain number problem, while the rotation invariance between beam clusters is built to solve the unknown phase difference problem in DOA estimation. The performance analysis is also provided to show the contributions of some key physical parameters on DOA estimation performance. The simulation results show that the DOA localization precision depends on the DOA estimation performance and Geometric Dilution of Precision (GDOP). The proposed method achieves a similar performance to the existing methods, but both the RF chain number and the computation complexity are significantly decreased.