Clustered Hybrid Accumulation Optimization Method for Target Detection With Multi-UAV Netted Radar

In the process of multiple unmanned aerial vehicle (UAV) netted radar detection, the sparse spatial distribution of each node in the multi-UAV netted radar results in differences in echo correlation between different nodes: echoes of closer nodes have stronger correlation, which is suitable for coherent accumulation, while echoes of farther nodes have weaker correlation, which is suitable for non-coherent accumulation. However, the key to obtain the improvement of the detection performance of the multi-UAV airborne radar lies in realizing the high-gain accumulation of radar echoes. In order to realize high-gain accumulation of echo signals, this paper utilizes clustered hybrid accumulation processing architecture (intra-cluster coherent accumulation and inter-cluster non-coherent accumulation) for multi-UAV netted radar. Then, we analyze the gain and computational amount in hybrid accumulation process and develop a clustered hybrid accumulation optimization model for these two metrics. To solve this optimization problem, we design a metaheuristic bi-directional clustering algorithm, which achieves better performance in improving hybrid accumulation gain and reducing the computational amount. Finally, the effectiveness of clustered hybrid accumulation optimization method is verified through simulation experiments.