A Multi-Objective Optimization Method for Radar Deployment Based on an Improved NSGA-II Algorithm
Yan Cheng, Jieyi Liu, Zhuowei Dong, Yongkang Wang, Yaoming Yang, Xiaolong Fan · 2023
For the resource allocation optimization problem of anti-deception jamming for networked radar, a population-adaptive improved NSGA-II algorithm based on hierarchical clustering truncation strategy is proposed. By minimizing the probability of networked radar being deceived and maximizing its coverage range, the optimal resource utilization method is obtained. The station deployment optimization method utilizes theoretical analysis of data fusion to determine the anti-jamming capability of networked radars. Theoretical values of the probability of being deceived at different spatial locations are calculated and used as inputs for the improved NSGA-II algorithm. Simulation results show that the proposed method can determine the optimal deployment locations under different conditions. This comprehensive approach addresses the resource optimization problem for anti-jamming in networked radar and enables effective deployment of networked radar systems.