多机雷达协同区域动态覆盖航迹优化方法

严俊坤; 白舸; 黄佳沁; 杜兰; 宋婷; 刘宏伟

doi:10.12000/JR22196

多机雷达协同区域动态覆盖航迹优化方法

DOI: 10.12000/JR22196

严俊坤^1, ,,
白舸¹,
黄佳沁²,
杜兰¹,
宋婷²,
刘宏伟¹

1.
西安电子科技大学雷达信号处理国家重点实验室西安 710071
2.
中国航空工业集团公司雷华电子技术研究所无锡 214063

基金项目: 国家自然科学基金(62071345, U21B2039)，中国航空科学基金(201920081002)

详细信息

作者简介:
严俊坤，博士，教授，博士生导师，研究方向为雷达智能信号处理、网络化雷达协同探测等

白　舸，博士生，研究方向为多机雷达协同探测

黄佳沁，硕士，工程师，研究方向为雷达资源管理、雷达数据处理等

杜　兰，博士，教授，研究方向为雷达目标识别、雷达信号处理等

宋　婷，硕士，工程师，研究方向为雷达智能化探测、雷达先进信号处理等

刘宏伟，博士，教授，研究方向为雷达目标分类与识别、认知网络、网络化协同探测等

通讯作者:
严俊坤 jkyan@xidian.edu.cn

责任主编：易伟 Corresponding Editor: YI Wei
中图分类号: TN959
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- 被引次数: 6
出版历程
- 收稿日期: 2022-09-27
- 修回日期: 2022-10-28
- 网络出版日期: 2022-11-04
- 刊出日期: 2023-06-28

Flight Path Optimization Method for Dynamic Area Coverage Based on Multi-aircraft Radars

1.
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
2.
AVIC Leihua Electronic Technology Research Institute, Wuxi 214063, China

Funds: The National Natural Science Foundation of China (62071345, U21B2039), The Aero Science Foundation of China (201920081002)

More Information

Corresponding author: YAN Junkun, jkyan@xidian.edu.cn

摘要

摘要: 传统面向区域覆盖的多机航迹优化方法大多针对静态环境建立优化模型，在复杂动态环境下面临着模型失配的挑战。因此，该文提出了一种多机雷达协同区域动态覆盖航迹优化方法。首先，该方法引入衰减因子来表征机载雷达对动态环境的实际覆盖效果，将动态覆盖背景下的区域覆盖率作为优化函数，并结合待优化多维航迹控制参数约束，构建了多机雷达协同区域动态覆盖航迹优化的数学模型。然后，采用随机优化法对协同区域动态覆盖航迹优化问题进行了求解。最后，仿真实验表明，相对于采用预设航迹的多机雷达搜索模式，所提航迹优化方法能够显著提高动态区域的动态覆盖性能，且相较于面向静态环境的传统航迹优化模型，动态覆盖性能平均提升约6%。
- 多机协同 /
- 雷达 /
- 区域动态覆盖 /
- 衰减因子 /
- 随机优化法
Abstract: Most traditional multi-aircraft flight path optimization methods are oriented toward area coverage, use static optimization models, and face the challenge of model mismatch under complex dynamic environments. Therefore, this study proposes a flight path optimization method for dynamic area coverage based on multi-aircraft radars. First, we introduce an attenuation factor to this method to characterize the actual coverage effect of airborne radar on a dynamic environment, and we take the area coverage rate under the dynamic area coverage background as the optimization function. After integrating the constraints of multi-dimensional flight path control parameters to be optimized, we built a mathematical model for dynamic area coverage flight path optimization based on multi-aircraft radars. Then, the stochastic optimization method is used to solve the flight path optimization problem of dynamic area coverage. Finally, the simulation results show that the proposed flight path optimization method can significantly improve the dynamic coverage performance in dynamic areas compared with the search mode using preset flight paths based on multi-aircraft radars. Compared with the traditional flight path optimization method oriented to static environments, the dynamic coverage performance of our proposed method is improved by approximately 6% on average.
- Multi-UAV collaboration /
- Radar /
- Dynamic area coverage /
- Attenuation factor /
- Stochastic optimization method

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图 1 覆盖区域示意图

Figure 1. Sketch of coverage area

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图 2 地面最大可探测区域示意图

Figure 2. Maximal detection ground area

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图 3 衰减因子随时刻差值变化曲线图

Figure 3. Curves of attenuation factor variation with time difference

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图 4 航迹控制参数示意图

Figure 4. Example of flight path control parameters

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图 5 零时刻无人机航迹控制参数示意图

Figure 5. Examples of flight path control parameters at the zero time

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图 6 各机组大致航迹示意图

Figure 6. Rough flight path of UAV groups

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图 7 机组有效覆盖率变化曲线图

Figure 7. Curves of effective coverage rate variation of UAV groups

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图 8 预设航迹示意图

Figure 8. Preset flight path

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图 9 传统静态优化方法所得大致航迹图

Figure 9. Rough flight path of traditional static optimization method

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图 10 3种搜索方法有效覆盖率曲线对比图

Figure 10. Comparison between curves of effective coverage rate variation of three searching methods

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表 1 优化模型运行流程示意表

Table 1. Running process of the optimization model

步骤	流程内容
①	计算基于时刻n的各时刻 ${\alpha _i}\left( n \right)$ ， $i = 1,2, \cdots ,n$
②	设置待优化无人机编号 $m = 1$
③	基于待优化无人机时刻 $n - 1$ 的解，生成可行解数量为 ${q_{\max }}$ 的解空间 ${ {\boldsymbol{C} }_n} = \left\{ {{\boldsymbol{c}}'_1,{\boldsymbol{c}}'_2, \cdots ,{\boldsymbol{c}}_{ {q_{\max } } }'} \right\}$
④	求解待优化无人机时刻n的解 ${{\boldsymbol{c}}_n} \in {{\boldsymbol{C}}_n}$ ，使 ${{\boldsymbol{c}}_n}$ 对应的有效覆盖率为 ${{\boldsymbol{C}}_n}$ 中最大
⑤	令 $m = m + 1$ ，若 $m \le M$ 则返回步骤③
⑥	令 $n = n + 1$ ，优化程序进入下一时刻

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表 2 仿真参数设定表

Table 2. Simulation parameter settings

参数	设定值
${R_0}$	90 m
${\varphi _0}$	$\dfrac{2}{3}\pi$
${t_d}$	1 s
${L_{\max }}$	200 m
$\Delta {\psi _{\max }}$	$\dfrac{1}{4}\pi$
${n_\alpha }$	60 s
$\gamma$	10
${q_{\max }}$	100

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