面向组网雷达干扰任务的多机伴随式编队航迹预规划方法

doi:10.12305/j.issn.1001-506X.2024.08.28

摘要/Abstract

摘要：

针对多目标突防组网雷达系统场景, 为有效提高干扰效果以及突防成功率, 编队航迹规划尤为重要。因此, 首先构建航迹规划模型, 从飞行器自身约束、航迹安全性、机间协调以及任务完成效果4个方面出发, 结合多机伴随式编队及其所处环境特点, 提出较为完备的航迹规划准则, 形成一个新的整体目标函数; 其次, 为有效描述每架飞机的机动特性以及伴飞干扰机与目标飞机间的联系, 提高算法搜索能力, 提出基于多球面矢量(multi-spherical vector-based, MS)方法; 为进一步提高算法的探索和开发能力, 提出多面球矢量逐航迹点学习混合粒子群优化(multi-spherical vector-based hybrid particle swarm optimization with track point by track point learning, TLHPSO)算法, 并将两者相结合, 形成基于多面球矢量的逐航迹点学习混合粒子群优化(MS-based hybrid particle swarm optimization with track point by track point learning, MS-TLHPSO)航迹规划方法; 最后, 构建相应仿真场景进行验证。对比结果表明, MS方法以及TLHPSO优化算法在寻优能力上具有明显优势; 同时, 所提算法在不同初始场景下最优解的平均值均优于其他算法, 充分说明所提算法能够在保证稳定性的前提下规划具有更高可信度的编队航迹。

关键词: 组网雷达, 编队航迹, 规划准则, 多球面矢量, 逐航迹点学习

Abstract:

For the multi-target penetrating netted radar system scenario, in order to effectively improve the interference effect and penetration success rate, formation trajectory planning is particularly important. Therefore, this paper firstly builds a trajectory planning model. Starting from the four aspects of the aircraft's own constraints, trajectory safety, constraints among aircrafts, and task completion effect, combined with the characteristics of the multi-syndrome jammers formation and its environment, a relatively complete trajectory planning criterion is proposed to form a new overall objective function. Secondly, in order to effectively describe the maneuver properties of each aircraft and the connection between the accompanying jammer and the target aircraft, and improve the algorithm' s search ability, a multi-spherical vector-based (MS) method is proposed. At the same time, in order to further improve the exploration and development capabilities of the proposed algorithm, a hybrid particle swarm optimization with track point by track point learning (TLHPSO) algorithm is proposed, and the two algorithms are combined to form MS-TLHPSO trajectory planning method. Finally, the simulation scenarios are constructed for verification. The comparison results show that the MS method and the TLHPSO optimization algorithm have obvious advantages in the ability to find the optimal value. The proposed algorithm in this paper is superior to other algorithms in terms of the average value of the optimal solution in different initial scenarios, which fully shows that the algorithm in this paper can plan formation tracks with higher reliability under the premise of ensuring stability.

Key words: netted radar, formation trajectory, planning criterion, multi-spherical vector, track point by track point learning

中图分类号:

TN974

邹玮琦, 牛朝阳, 刘伟, 王艳云, 湛嘉祺. 面向组网雷达干扰任务的多机伴随式编队航迹预规划方法[J]. 系统工程与电子技术, 2024, 46(8): 2807-2819.

Weiqi ZOU, Chaoyang NIU, Wei LIU, Yanyun WANG, Jiaqi ZHAN. Multi-syndrome jammers formation trajectory preplanning method for netted radar jamming task[J]. Systems Engineering and Electronics, 2024, 46(8): 2807-2819.

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相关参数	具体数值
最大迭代次数Ger	300
粒子数I	100
惯性系数最大值W_max	1
惯性系数最小值W_min	0.4
学习因子最大值c_max	2.5
学习因子最小值c_min	1.5
评估周期T₁	4
逐航迹点学习周期T₂	50

威胁类型	中心坐标	威胁距离
雷达节点1	(2 000, 3 500)	3 000
雷达节点2	(3 500, 500)	3 000
雷达节点3	(4 500, 5 000)	3 000
雷达节点4	(4 500, 7 500)	3 000
雷达节点5	(6 600, 2 000)	3 000
雷达节点6	(8 000, 5 500)	3 000

案例	威胁类型	起点坐标	终点坐标
案例1	目标飞机1	(2 000, 1 000, 1 500)	(4 850, 7 900, 1 600)
	伴飞干扰机1	(2 040, 970, 1 460)	(4 890, 7 870, 1 560)
	伴飞干扰机2	(1 960, 1 040, 1 450)	(4 820, 7 930, 1 560)
	目标飞机2	(5 500, 1 100, 1 700)	(5 000, 8 000, 1 500)
	伴飞干扰机3	(5 460, 1 070, 1 650)	(4 970, 7 970, 1 460)
案例2	目标飞机1	(1 000, 5 000, 1 500)	(7 700, 8 000, 1 600)
	伴飞干扰机1	(1 030, 4 970, 1 460)	(7 730, 7 970, 1 560)
	伴飞干扰机2	(960, 5 030, 1 460)	(7 670, 8 030, 1 560)
	目标飞机2	(2 000, 1 000, 1 600)	(8 000, 7 700, 1 500)
	伴飞干扰机3	(1 970, 970, 1 560)	(7 970, 7 670, 1 460)

算法	最佳值	最差值	平均值	标准值
PSO	61 408.48	121 548.60	97 988.52	14 890.010
HIPSO-MSOS	71 944.86	115 643.50	88 804.44	11 697.120
TLHPSO	53 232.81	96 403.49	75 743.96	12 239.290
MSPSO	38 532.29	77 287.50	49 390.56	9 067.083
MS-HIPSO-MSOS	34 992.36	52 625.60	40 684.50	4 381.268
MS-TLHPSO	28 267.81	34 743.45	31 519.64	1 770.986

算法	最佳值	最差值	平均值	标准值
PSO	77 337.96	111 948.1	92 770.48	10 825.57
HIPSO-MSOS	67 573.74	108 921.2	85 384.60	11 694.55
TLHPSO	49 503.04	92 075.05	68 768.80	11 709.960
MSPSO	35 662.75	49 534.39	40 123.17	3 826.639
MS-HIPSO-MSOS	31 403.77	41 296.28	34 359.15	2 988.954
MS-TLHPSO	29 013.56	36 412.56	31 575.41	2 098.378