Interval model updating(IMU)methods have been widely used in uncertain model updating due to their low requirements for sample data.However,the surrogate model in IMU methods mostly adopts the one-time construction me...Interval model updating(IMU)methods have been widely used in uncertain model updating due to their low requirements for sample data.However,the surrogate model in IMU methods mostly adopts the one-time construction method.This makes the accuracy of the surrogate model highly dependent on the experience of users and affects the accuracy of IMU methods.Therefore,an improved IMU method via the adaptive Kriging models is proposed.This method transforms the objective function of the IMU problem into two deterministic global optimization problems about the upper bound and the interval diameter through universal grey numbers.These optimization problems are addressed through the adaptive Kriging models and the particle swarm optimization(PSO)method to quantify the uncertain parameters,and the IMU is accomplished.During the construction of these adaptive Kriging models,the sample space is gridded according to sensitivity information.Local sampling is then performed in key subspaces based on the maximum mean square error(MMSE)criterion.The interval division coefficient and random sampling coefficient are adaptively adjusted without human interference until the model meets accuracy requirements.The effectiveness of the proposed method is demonstrated by a numerical example of a three-degree-of-freedom mass-spring system and an experimental example of a butted cylindrical shell.The results show that the updated results of the interval model are in good agreement with the experimental results.展开更多
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 n...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-toNoise 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.展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.12272211,12072181,12121002)。
文摘Interval model updating(IMU)methods have been widely used in uncertain model updating due to their low requirements for sample data.However,the surrogate model in IMU methods mostly adopts the one-time construction method.This makes the accuracy of the surrogate model highly dependent on the experience of users and affects the accuracy of IMU methods.Therefore,an improved IMU method via the adaptive Kriging models is proposed.This method transforms the objective function of the IMU problem into two deterministic global optimization problems about the upper bound and the interval diameter through universal grey numbers.These optimization problems are addressed through the adaptive Kriging models and the particle swarm optimization(PSO)method to quantify the uncertain parameters,and the IMU is accomplished.During the construction of these adaptive Kriging models,the sample space is gridded according to sensitivity information.Local sampling is then performed in key subspaces based on the maximum mean square error(MMSE)criterion.The interval division coefficient and random sampling coefficient are adaptively adjusted without human interference until the model meets accuracy requirements.The effectiveness of the proposed method is demonstrated by a numerical example of a three-degree-of-freedom mass-spring system and an experimental example of a butted cylindrical shell.The results show that the updated results of the interval model are in good agreement with the experimental results.
文摘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-toNoise 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.
