Journal of System Simulation ›› 2018, Vol. 30 ›› Issue (12): 4703-4711.doi: 10.16182/j.issn1004731x.joss.201812026

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Collision Avoidance Performance Improvement for TCAS in Multi-Aircraft Situations Based on State Prediction

Tang Jun, Zhu Fen, Wan Yu, Lao Songyang   

  1. College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China
  • Received:2018-06-28 Revised:2018-07-29 Online:2018-12-10 Published:2019-01-03

Abstract: Currently the density of the airspace increases with the air traffic flow increasing significantly, and therefore the conflict occurrence possibility of two or multiple aircraft raises. However, the traffic collision avoidance system (TCAS) may not be able to resolve all the collision problems of multi-threat situation. There is an active demand to improve its performance in multi-threat situation. This paper mathematically describes the traditional TCAS anti-collision mechanism, and achieves its expansion in the horizontal direction, thus proposes the improvement algorithm of collision avoidance performance for TCAS in multi-aircraft situations based on the state prediction. Adjusting the height and vertical speed are used as the core in this algorithm, course change and multiple aircraft cooperation are combined to choose the optimization strategy to avoid collision. Simulation results demonstrate the effectiveness of our approaches.

Key words: air traffic management, TCAS, multi-aircraft situations, state prediction, collision risk

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