Open Access
 Issue JNWPU Volume 39, Number 4, August 2021 761 - 769 https://doi.org/10.1051/jnwpu/20213940761 23 September 2021

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## 1 无人机感知与规避S&A系统

### 1.2 功能实现

 图1S&A系统功能模型架构实现

## 3 冲突探测方法分析

### 3.1 水平初选

S0≥0则表示两机正在逐渐远离或持续保持间隔, 而S0 < 0则表示两机需要进一步判断。

### 3.2 水平探测

 图2水平冲突探测图

### 3.3 垂直探测

B的相对航迹线对应斜率为

S1≥0且S2≥0, 则表明在垂直面上, AB之间将无冲突产生; 反之, 则需要进行冲突解脱。

 图3垂直冲突探测图

## 5 仿真分析

### 5.1 冲突探测仿真

1) 目标飞行器速度值: vhori∈[180, 360], vvert∈[-30, 30], 单位为km/h;

2) 目标飞行器水平航向：与X轴夹角ϕ∈[0, 2π];

3) 目标飞行器坐标: x∈(9.26cosθ, 40cosθ), y∈(9.26sinθ, 40sinθ), θ∈(0, 2π], z∈[-1.5, 1.5], 单位为km。

 图4冲突探测结果
 图5冲突概率分布

### 5.2 冲突解脱仿真

 图6两机冲突解脱

 图7四机冲突解脱
 图8收敛过程对比

## References

1. Lyu Yang, Kang Tongna, Pan Quan, et al. UAV sense and avoidance: concepts, technologies, and systems[J]. Scientia Sinica Informationis, 2019, 49: 520–537 [Article] (in Chinese) [Google Scholar]
2. 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 [Article] (in Chinese) [Google Scholar]
3. Arteaga R A, Cavalin M, Dandachy M, et al. Application of an ADS-B sense and avoid algorithm[C]//AIAA Flight Testing Conference, 2016 [Google Scholar]
4. Li Zhuyuan. Study for autonomous obstacle-avoiding algorithm of civilian UAVs[D]. Guanghan: Civil Aviation Flight University of China, 2018 (in Chinese) [Google Scholar]
5. Sahawneh L R, Duffield M O, Beard R W, et al. Detect and avoid for small unmanned aircraft systems using ADS-B[J]. Air Traffic Control Quarterly, 2015, 23(2/3): 203–240 [Google Scholar]
6. Fasano G, Accado D, Moccia A, et al. Sense and avoid for unmanned aircraft systems[J]. IEEE Aerospace and Electronic Systems Magazine, 2016, 31(11): 82–110 [Article] [Google Scholar]
7. Yu X, Zhang Y M. Sense and avoid technologies with applications to unmanned aircraft systems: review and prospects[J]. Progress in Aerospace Sciences, 2015, 74: 152–166 [Article] [NASA ADS] [CrossRef] [Google Scholar]
8. Ni Yude, Ma Yushen, Liu Ping. Aircraft conflict detection based on ADS-B[J]. Journal of Civil Aviation University of China, 2014, 32(5): 31–35, 44 [Article] (in Chinese) [Google Scholar]
9. Jia Yuncong. Research on the UAV conflict avoidance method and hedging equipment[D]. Shijiazhuang: Hebei University of Science and Technology, 2018 (in Chinese) [Google Scholar]
10. Zhang Zhongguang, Shi Hongwei. A conflict alert algorithm based on ADS-B information[J]. Computer and Modernization, 2015(11): 79–83 [Article] (in Chinese) [Google Scholar]
11. Jie Dong. Research on technologies of UAV conflict detection and resolution strategy[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2019 (in Chinese) [Google Scholar]
12. Zhang Songcan, Pu Jiexin, Si Yanna, et al. Survey on application of ant colony algorithm in path planning of mobile robot[J]. Computer Engineering and Applications, 2020, 56(8): 10–19 [Article] (in Chinese) [Google Scholar]
13. Li Xianqiang, Ma Rong, Zhang Shen, et al. Improved design of ant colony algorithm and its application in path planning[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(suppl 2): 213–219 [Article] (in Chinese) [Google Scholar]
14. Liu Yang, Zhang Weiguo, Li Guangwen, et al. Path Planning of UAV in dynamic environment[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(2): 252–256 [Article] (in Chinese) [Google Scholar]
15. Zhang C, Hu C, Feng J, et al. A self-heuristic ant-based method for path planning of unmanned aerial vehicle in complex 3D space with dense u-type obstacles[J]. IEEE Access, 2019, 99: 1–1 [Google Scholar]
16. Li Li, Li Hong, Shan Ningbo. Path planning based on improved ant colony algorithm with multiple inspired factor[J]. Computer Engineering and Applications, 2019, 55(5): 219–225, 250 [Article] (in Chinese) [Google Scholar]
17. 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 [Article] (in Chinese) [Google Scholar]
18. Zhang Shuran. Research on UAV track planning based on swarm intelligence algorithm[D]. Chengdu: University of Electronic Science and Technology of China, 2020 (in Chinese) [Google Scholar]

## All Figures

 图1S&A系统功能模型架构实现 In the text
 图2水平冲突探测图 In the text
 图3垂直冲突探测图 In the text
 图4冲突探测结果 In the text
 图5冲突概率分布 In the text
 图6两机冲突解脱 In the text
 图7四机冲突解脱 In the text
 图8收敛过程对比 In the text

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.