In order to improve the accuracy of free flight conflict detection and reduce the false alarm rate, an improved flight conflict detection algorithm is proposed based on Gauss-Hermite particle filter（GHPF）. The algor...In order to improve the accuracy of free flight conflict detection and reduce the false alarm rate, an improved flight conflict detection algorithm is proposed based on Gauss-Hermite particle filter（GHPF）. The algorithm improves the traditional flight conflict detection method in two aspects：（i） New observation data are integrated into system state transition probability, and Gauss-Hermite Filter（GHF） is used for generating the importance density function.（ii） GHPF is used for flight trajectory prediction and flight conflict probability calculation. The experimental results show that the accuracy of conflict detection and tracing with GHPF is better than that with standard particle filter. The detected conflict probability is more precise with GHPF, and GHPF is suitable for early free flight conflict detection.展开更多
Describing spatial safety status is crucial for high-density air traffic involving multiple unmanned aerial vehicles （UAVs） in a complex environment. A probabilistic approach is proposed to measure safety situation ...Describing spatial safety status is crucial for high-density air traffic involving multiple unmanned aerial vehicles （UAVs） in a complex environment. A probabilistic approach is proposed to measure safety situation in congested airspace. The occupancy distribution of the airspace is represented with conflict probability between spatial positions and UAV. The concept of a safety envelope related to flight performance and response time is presented first instead of the conventional fixed-size protected zones around aircraft. Consequently, the conflict probability is performance-dependent, and effects of various UAVs on safety can be distinguished. The uncertainty of a UAV future position is explicitly accounted for as Brownian motion. An analytic approximate algorithm for the conflict probability is developed to decrease the computational consumption. The relationship between safety and flight performance are discussed for different response times and prediction intervals. To illustrate the applications of the approach, an experiment of three UAVs in formation flight is performed. In addition, an example of trajectory planning is simulated for one UAV flying over airspace where five UAVs exist. The validation of the approach shows its potential in guaranteeing flight safety in highly dynamic environment.展开更多
基金Supported by the Joint Project of National Natural Science Foundation of ChinaCivil Aviation Administration of China(U1333116)
文摘In order to improve the accuracy of free flight conflict detection and reduce the false alarm rate, an improved flight conflict detection algorithm is proposed based on Gauss-Hermite particle filter（GHPF）. The algorithm improves the traditional flight conflict detection method in two aspects：（i） New observation data are integrated into system state transition probability, and Gauss-Hermite Filter（GHF） is used for generating the importance density function.（ii） GHPF is used for flight trajectory prediction and flight conflict probability calculation. The experimental results show that the accuracy of conflict detection and tracing with GHPF is better than that with standard particle filter. The detected conflict probability is more precise with GHPF, and GHPF is suitable for early free flight conflict detection.
基金supported by the National Basic Research Program of China (No.2011CB707002)
文摘Describing spatial safety status is crucial for high-density air traffic involving multiple unmanned aerial vehicles （UAVs） in a complex environment. A probabilistic approach is proposed to measure safety situation in congested airspace. The occupancy distribution of the airspace is represented with conflict probability between spatial positions and UAV. The concept of a safety envelope related to flight performance and response time is presented first instead of the conventional fixed-size protected zones around aircraft. Consequently, the conflict probability is performance-dependent, and effects of various UAVs on safety can be distinguished. The uncertainty of a UAV future position is explicitly accounted for as Brownian motion. An analytic approximate algorithm for the conflict probability is developed to decrease the computational consumption. The relationship between safety and flight performance are discussed for different response times and prediction intervals. To illustrate the applications of the approach, an experiment of three UAVs in formation flight is performed. In addition, an example of trajectory planning is simulated for one UAV flying over airspace where five UAVs exist. The validation of the approach shows its potential in guaranteeing flight safety in highly dynamic environment.
