Optimization of digital multi-beamforming for space-based ADS-B using distributed cooperative coevolution with an adaptive grouping strategy

Abstract

Space-based Automatic Dependent Surveillance-Broadcast (ADS-B) technology can eliminate the blind spots of terrestrial ADS-B systems because of its global coverage capability. However, the space-based ADS-B system faces new problems such as extremely low Signal-to-Noise Ratio (SNR) and serious co-channel interference, which result in long update intervals. To minimize the position message update interval at an update probability of 95% with full coverage constraint, this paper presents an optimization model of digital multi-beamforming for space-based ADS-B. Then, a coevolution method DECCG_A&A is proposed to enhance the optimization efficiency by using an improved adaptive grouping strategy. The strategy is based on the locations of uncovered areas and the aircraft density under the coverage of each beam. Simulation results show that the update interval can be effectively controlled to be below 8 seconds compared with other existing methods, and DECCG_A&A is superior in convergence to the Genetic Algorithm (GA) as well as the coevolution algorithms using other grouping strategies. Overall, the proposed optimization model and method can significantly reduce the update interval, thus improving the surveillance performance of space-based ADS-B for air traffic control.