ADS-B IN based Conflict Prediction and Conflict-free Trajectory Planning for Multi-aircraft

doi:10.16182/j.issn1004731x.joss.17-0266

Abstract

Abstract: For free flight of future, it is necessary to continuously detect conflicts and plan safe flight paths. Through in-depth analysis of detection principle of TCAS, combined with the characteristics of ADS-B data, the target within the scope of ADS-B IN surveillance is classified and given a risk factor, and the TCAS function is implemented in the ADS-B IN simulation software. For complex conflict scenarios with multi-aircraft, by meshing the conflict region and discretizing the flight procedure, the genetic algorithm is then used to calculate the optimal conflict-free trajectory based on risk factors. Two common multi-aircraft conflict scenarios are simulated, and the results show that the algorithm proposed in this article can solve the multi-aircraft conflict issues quickly and efficiently.

Key words: automatic dependent surveillance-broadcast (ADS-B), free flight, conflict prediction, genetic algorithm, trajectory planning

CLC Number:

TP391.9

Zhang Siyuan, Li Xianying, Shen Xiaoyun. ADS-B IN based Conflict Prediction and Conflict-free Trajectory Planning for Multi-aircraft[J]. Journal of System Simulation, 2019, 31(8): 1627-1635.

References

[1] Rose C E, Panken A D, Harman W H, et al. TCAS surveillance performance analysis[C]. Digital Avionics Systems Conference IEEE. Salt Lake City, UT, USA: IEEE, 2010: 3.B.4-1-3.B.4-13.
[2] 李丹, 崔德光. 基于布朗运动的空中交通短期冲突探测[J]. 清华大学学报(自然科学版), 2008, 48(4): 477-481.
Li Dan, Cui Deguang.Air traffic control conflict detection algorithm based on Brownian motion[J]. Journal of Tsinghua University(Science and Technology), 2008, 48(4): 477-481.
[3] Kochenderfer M J, Edwards M W M, Espindle L P, et al. Airspace encounter models for estimating collision risk[J]. Journal of Guidance Control & Dynamics (S0731-5090), 2012, 33(2): 487-499.
[4] 翟文鹏, 李亚飞. 飞行目标航迹生成及冲突探测算法研究[J]. 中国安全科学学报, 2013, 23(10): 110-113.
Zhai Wenpeng, Li Yafei.Flying target track generation and conflict detection algorithm[J]. China Safety Science Journal, 2013, 23(10): 110-113.
[5] 张兆宁, 佐江丽. 基于布朗运动的自由飞行下碰撞风险研究[J]. 中国安全科学学报, 2012, 22(8): 43-47.
Zhang Zhaoning, Zuo Jiangli.Study on free flight collision risk based on Brownian motion[J]. China Safety Science Journal, 2012, 22(8): 43-47.
[6] 中国民用航空局. 中国民用航空ADS-B实施规划[S]. 北京: 中国民用航空局, 2015.
Civil Aviation Administration of China. China’s civil aviation ADS-B implementation plan[S]. Beijing: Civil Aviation Administration of China, 2015.
[7] Baek K, Bang H.ADS-B based trajectory prediction and conflict detection for air traffic management[J]. International Journal of Aeronautical & Space Sciences (S2093-274X), 2012, 13(3): 377-385.
[8] 郝帅, 朱代武, 陈肯. 基于ADS-B的飞行安全间隔方法研究[J]. 科学技术与工程, 2010, 10(33): 8333-8336.
Hao Shuai, Zhu Daiwu, Chen Ken.Study of Flight Separation Criteria in Air Traffic Control Based on ADS-B[J]. Science Technology and Engineering, 2010, 10(33): 8333-8336.
[9] 张晓燕, 陈亚青, 刘国毅, 等. ADS-B监视下飞行间隔评估方法研究[J]. 航空计算技术, 2011, 41(2): 13-15.
Zhang Xiaoyan, Chen Yaqing, Liu Guoyi, et al.Research on method of flight separation assessment based on ADS-B[J]. Aeronautical Computing Technique, 2011, 41(2): 13-15.
[10] Orefice M, Vito V D, Corraro F, et al.Aircraft conflict detection based on ADS-B surveillance data[C]. IEEE Metrology for Aerospace (MetroAeroSpace). Benevento, Italy: IEEE, 2014: 277-282.
[11] 倪壮, 肖刚, 敬忠良, 等. 改进蚁群算法的飞机冲突解脱路径规划方法[J]. 传感器与微系统, 2016, 35(4): 130-133.
Ni Zhuang, Xiao Gang, Jing Zhongliang, et al.Path planning method for aircrafts conflict resolution based on improved ant colony algorithm[J]. Transducer and Microsystem Technologies, 2016, 35(4): 130-133.
[12] 王洁宁, 袁志娟. 基于粒子群算法的飞行冲突解脱问题[J]. 中国民航大学学报, 2010, 28(4): 1-4.
Wang Jiening, Yuan Zhijuan.Study on resolution of flight conflicts based on particle swarm optimization[J]. Journal of Civil Aviation University of China, 2010, 28(4): 1-4.
[13] 王渊, 孙秀霞, 刘树光, 等. 基于改进人工蜂群算法的多机飞行冲突解脱策略[J]. 空军工程大学学报(自然科学版), 2014, 15(3): 10-14.
Wang Yuan, Sun Xiuxia, Liu Shuguang, et al.Research on multi-aircraft confliction resolution based on a modified artificial bee colony algorithm[J]. Journal of Air Force Engineering University (Natural Science Edition), 2014, 15(3): 10-14.
[14] 陈伟锋, 邵之江. 基于析取关系直接变换的冲突解脱方法[J]. 航空学报, 2014, 35(4): 1122-1133.
Chen Weifeng, Shao Zhijiang.Direct disjunction transcription based conflict resolution approach[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(4): 1122-1133.
[15] 孙禾, 蔡月茹, 周沅. 飞机多机飞行冲突状态预测研究[J]. 计算机仿真, 2016, 33(6): 59-63,409.
Sun He, Cai Yueru, Zhou Yuan.A Study on multi-conflict flight mode forecast[J]. Computer Simulation, 2016, 33(6): 59-63,409.
[16] 刘永兰, 李为民, 吴虎胜, 等. 基于狼群算法的无人机航迹规划[J]. 系统仿真学报, 2015, 27(8): 1838-1843.
Liu Yonglan, Li Weimin, Wu Husheng, et al.Track Planning for Unmanned Aerial Vehicles Based on Wolf Pack Algorithm[J]. Journal of System Simulation, 2015, 27(8): 1838-1843.
[17] RTCA. DO-263 Application of Airborne Conflict Management: Detection, Prevention & Resolution[S]. Washington: DC, 2000.
[18] Kuchar J K, Yang L C.A review of conflict detection and resolution modeling methods[J]. IEEE Transactions on intelligent transportation systems (S1524-9050), 2000, 1(4): 179-189.
[19] Gariel M, Hansman R J, Frazzoli E.Impact of GPS and ADS-B Reported Accuracy on Conflict Detection Performance in Dense Traffic[C]. AIAA Aviation Technology, Integration, and Operations. Virginia Beach, VA, USA: AIAA, 2011.
[20] FAA. Introduction to TCAS II, version 7.1[S]. USA: FAA, 28 February 2011.