Research on Distributed Cooperative Intelligent Spectrum Sensing of UAV Cluster

Abstract

Spectrum sensing is important to improving the survivability of unmanned aerial vehicles (UAVs) in complex electromagnetic environments. At a low signal-to-noise ratio (SNR), a UAV cluster has more prominent advantages in cluster distributed cooperative sensing than a single UAV. Aiming at this urgent need, joint optimal design is carried out for adaptive spectrum sensing algorithm and distributed estimation algorithm to realize the distributed cooperative intelligent spectrum sensing of UAV cluster. In this paper, the adaptive theory is analyzed first, and the performance of the conventional energy-aware sensing method and the least mean square (LMS) spectrum sensing algorithm is compared. In a complex electromagnetic environment, it is proposed to replan the real-time network by deleting erroneous data nodes in order to eliminate parameter estimation deviations caused by data errors. Under the condition of ensuring detection probability, the fast spectrum sensing algorithm based on SNR estimation is optimized by adaptively selecting and setting the SNR threshold to solve the problem of complex and slow calculation. The superiority of distributed spectrum estimation algorithm without erroneous data nodes is verified at a low SNR, showing that the algorithm has a good steady-state error curve and avoids the impact of data errors on detection results. In addition, the effectiveness of optimizing the fast spectrum sensing algorithm by selecting and setting the SNR threshold is verified to improve the distributed cooperative intelligent spectrum sensing rate of UAV cluster.