基于低冗余度染色体编码的族群无人机SAR二维成像任务分配方法

任航; 孙稚超; 杨建宇; 武俊杰

doi:10.12000/JR24218

基于低冗余度染色体编码的族群无人机SAR二维成像任务分配方法

DOI: 10.12000/JR24218

任航¹,
孙稚超^{1, 2},
杨建宇¹,
武俊杰^1, ,

1.
电子科技大学信息与通信工程学院成都 611731
2.
电子科技大学长三角研究院(衢州) 衢州 324003

基金项目: 国家自然科学基金(61901088, 61922023, 62231006, 62471098, 62431008)，中央高校基本科研业务费项目(ZYGX2022J005)，衢州市财政资助科研项目(2023D042)

详细信息

作者简介:
任　航，博士生，研究方向为双多基合成孔径雷达、压缩感知理论、优化理论和算法等

孙稚超，副教授，研究方向为最优化方法及其应用、双多基合成孔径雷达、星载合成孔径雷达信号处理等

杨建宇，教授，博士生导师，研究方向为雷达信号处理、合成孔径雷达成像等

武俊杰，教授，博士生导师，研究方向为合成孔径雷达成像、双多基合成孔径雷达、雷达信号处理等

通讯作者:
武俊杰 junjie_wu@uestc.edu.cn

责任主编：丁泽刚 Corresponding Editor: DING Zegang
中图分类号: TN958.3
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出版历程
- 收稿日期: 2024-11-06
- 修回日期: 2024-12-26

A Task Allocation Method for Swarm UAV SAR based on Low Redundancy Chromosome Encoding

REN Hang¹,
SUN Zhichao^{1, 2},
YANG Jianyu¹,
WU Junjie^{1
, ,}

1.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2.
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China

Funds: The National Natural Science Foundation of China (61901088, 61922023, 62231006, 62471098, 62431008), The Fundamental Research Funds for Central Universities (ZYGX2022J005), The Municipal Government of Quzhou (2023D042)

More Information

Corresponding author: WU Junjie, junjie_wu@uestc.edu.cn

摘要

摘要: 该文针对族群无人机SAR系统的任务分配问题，提出了一种基于低冗余度染色体编码的族群无人机SAR任务分配方法。该方法针对SAR成像任务的特有问题分析了成像性能与成像几何构型之间的内在联系，并据此建立了考虑成像性能的路径函数，将族群无人机SAR任务分配问题建模为广义均衡多旅行商问题；然后，采用冗余度较低的两部分染色体编码方式来表征任务分配方案，提高遗传算法的搜索效率和准确性。针对实际应用中可能发生的意外情况，该文还提出了一种融合了合同网算法和注意力机制的动态任务分配策略，该策略能够根据实际情况灵活调整任务分配方案，确保系统的鲁棒性。仿真实验验证了该文所提方法的有效性。
- 族群无人机 /
- 合成孔径雷达 /
- 任务分配 /
- 遗传算法 /
- 染色体编码
Abstract: This paper addresses the task allocation problem in swarm Unmanned Aerial Vehicle (UAV) Synthetic Aperture Radar (SAR) systems and proposes a method based on low-redundancy chromosome encoding. It starts with a thorough analysis of the relationship between imaging performance and geometric configurations in SAR imaging tasks and accordingly constructs a path function that reflects imaging resolution performance. The task allocation problem is then formulated as a generalized, balanced multiple traveling salesman problem. To enhance the search efficiency and accuracy of the algorithm, a two-part chromosome encoding scheme with low redundancy is introduced. Additionally, considering possible unexpected situations and dynamic changes in practical applications, a dynamic task allocation strategy integrating a contract net protocol and attention mechanisms is proposed. This method can flexibly adjust task allocation strategies based on actual conditions, ensuring the robustness of the system. Simulation experiments validate the effectiveness of the proposed method.
- Swarm UAV /
- Synthetic Aperture Radar (SAR) /
- Task allocation /
- Genetic algorithm /
- Chromosome encoding

HTML全文

图 1 族群无人机SAR任务分配场景

Figure 1. Scenario of task allocation for swarm UAV SAR

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图 2 几何观测示意图

Figure 2. Geometric observation diagram

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图 3 飞行路径示意图

Figure 3. Flight path diagram

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图 4 单染色体编码方案示例

Figure 4. Example of single chromosome coding scheme

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图 5 双染色体编码方案示例

Figure 5. Example of dual chromosome coding scheme

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图 6 两部分染色体编码方案示例

Figure 6. Example of two-part chromosome coding scheme

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图 7 两点中央交叉

Figure 7. Two-point central crossover

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图 8 反向变异

Figure 8. Inverse mutation

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图 9 低冗余度染色体编码算法流程图

Figure 9. Flowchart of low redundancy chromosome encoding algorithm

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图 10 引入注意力机制的合同网算法

Figure 10. Contract network algorithm incorporating attention mechanism

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图 11 目标分布情况

Figure 11. Target distribution

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图 12 任务分配结果

Figure 12. Task allocation results

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图 13 本文所提方法各性能指标随迭代次数变化的曲线

Figure 13. The curves of the performance indicators of the proposed method changing with the number of iterations

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图 14 单染色体编码法各性能指标随迭代次数变化的曲线

Figure 14. The curves of the performance indicators of the single chromosome coding method changing with the number of iterations

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图 15 双染色体编码法各性能指标随迭代次数变化的曲线

Figure 15. The curves of the performance indicators of the dual chromosome coding method changing with the number of iterations

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图 16 粒子群算法各性能指标随迭代次数变化的曲线

Figure 16. The curves of the performance indicators of the particle swarm optimization algorithm changing with the number of iterations

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图 17 模拟退火算法各性能指标随迭代次数变化的曲线

Figure 17. The curves of the performance indicators of simulated annealing algorithm changing with the number of iterations

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图 18 目标点T₂的成像结果

Figure 18. The imaging results of target point T₂

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图 19 目标点T₄的成像结果

Figure 19. The imaging results of target point T₄

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图 20 目标点T₈的成像结果

Figure 20. The imaging results of target point T₈

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图 21 不同成像任务需求下的任务分配结果

Figure 21. Task allocation results under different imaging task requirements

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图 22 动态任务分配方案

Figure 22. Dynamic task allocation scheme

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表 1 系统参数

Table 1. System parameters

参数	符号	数值
波长(m)	$\lambda $	0.03
载波频率(GHz)	${f_{{\mathrm{c}}}}$	10
带宽(MHz)	${B_{\rm r}}$	300
合成孔径时间(s)	${T_{\rm a}}$	2
发射站位置(km)	${P_{\rm T}}$	(0,–35786,35786)
接收站高度(m)	${H_{\rm R}}$	3000
接收站速度(m/s)	${v_{\rm R}}$	50

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表 2 任务执行情况

Table 2. Task execution status

无人机	飞行路径(km)	成像任务数
UAV 1	159.35	4
UAV 2	169.75	2
UAV 3	148.85	4
UAV 1—UAV 3总和	477.95	10

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表 3 不同任务分配方法得到方案的性能指标

Table 3. Performance indicators of solutions obtained from different task allocation methods

方法	总飞行路径(km)	最大飞行路径(km)	总分辨面积(m²)
本文所提方法	477.94	169.75	13.69
单染色体编码法	493.38	192.69	18.95
双染色体编码法	576.01	258.31	21.89
粒子群算法	506.10	181.64	16.00
模拟退火算法	485.42	181.41	16.79

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表 4 各任务分配方法所得成像结果的图像熵

Table 4. Image entropy of imaging results obtained by various task allocation methods

目标点	本文所提方法	单染色体编码法	双染色体编码法
T₂	6.8240	6.1817	6.3874
T₄	5.9558	5.7366	5.6229
T₈	6.1061	5.8762	5.9497

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表 5 成像任务需求

Table 5. Imaging task requirements

任务	分辨夹角需求	幅宽需求(m)	NESZ需求(dB)
1	40°～140°	2000	–10
2	45°～135°	600	–20
3	40°～140°	1000	–20
4	60°～120°	2000	–15
5	60°～120°	1200	–18
6	45°～135°	1800	–15
7	80°～100°	1200	–18
8	40°～140°	2000	–15
9	80°～100°	600	–22
10	80°～100°	1000	–20

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