目标动态威胁度驱动的分布式组网相控阵雷达资源优化分配算法

宋晓程; 李陟; 任海伟; 易伟

doi:10.12000/JR22240

目标动态威胁度驱动的分布式组网相控阵雷达资源优化分配算法

DOI: 10.12000/JR22240

宋晓程¹,
李陟¹,
任海伟²,
易伟^2, ,

1.
北京电子工程总体研究所北京 100854
2.
电子科技大学信息与通信工程学院成都 611731

基金项目: 国家自然科学基金(62231008, U19B2017)，中央高校基本科研业务费专项资金(ZYGX2020ZB029)

详细信息

作者简介:
宋晓程，高级工程师，硕士，主要研究方向为制导与控制、指挥控制与作战筹划设计

李　陟，中国科学院院士，博士生导师，主要研究方向为武器系统总体与探测制导技术

任海伟，硕士生，主要研究方向为雷达信号处理、雷达资源管理技术

易　伟，教授，博士生导师，主要研究方向为雷达协同探测、低可观测目标探测技术

通讯作者:
易伟 kussoyi@gmail.com

责任主编：严俊坤 Corresponding Editor: YAN Junkun
中图分类号: TN958.92
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出版历程
- 收稿日期: 2022-12-22
- 修回日期: 2023-02-09
- 网络出版日期: 2023-02-22
- 刊出日期: 2023-06-28

Threat-driven Resource Allocation Algorithm for Distributed Netted Phased Array Radars

1.
Beijing Institute of Electronic Engineering, Beijing 100854, China
2.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: The National Natural Science Foundation of China (62231008, U19B2017), The Fundamental Research Funds for the Central Universities (ZYGX2020ZB029)

More Information

Corresponding author: YI Wei, kussoyi@gmail.com

摘要

摘要: 针对分布式组网相控阵雷达多目标跟踪(MTT)场景，该文提出一种目标动态威胁度驱动的波束分配与驻留时间联合优化算法。首先，在采用分布式组网架构的基础上，推导包含波束和驻留时间分配的贝叶斯克拉美罗界(BCRLB)。其次，基于目标实时运动状态构建综合威胁度评估尺度，按照威胁度为不同目标设计基于跟踪精度参考门限和贡献度的效用函数，以此衡量资源在多目标间的优先分配关系。随后，将该效用函数结合组网相控阵雷达系统资源，建立了目标动态威胁度驱动的波束分配与驻留时间联合优化模型。最后，采用一种基于奖励的迭代下降搜索算法进行求解。仿真结果表明，相较于平均资源分配方法，所提算法具备对若干差异性目标的跟踪精度需求感知能力，能够在基于多目标威胁度评估的基础上，有针对性地分配跟踪资源，从而有效提高组网相控阵雷达面对不同威胁度目标时的综合跟踪精度。
- 组网相控阵雷达 /
- 目标威胁度评估 /
- 贝叶斯克拉美罗界 /
- 多目标跟踪 /
- 资源分配
Abstract: For the Multi-Target Tracking (MTT) of distributed netted phased array radars, this paper proposes a joint beam and dwell time allocation algorithm driven by dynamic threats. First, a Bayesian Cramer-Rao Lower Bound (BCRLB), including beam and dwell time allocation, is derived. Then, a comprehensive threat evaluation scale is constructed based on the real-time motion state of the target, and a utility function based on the tracking accuracy reference threshold and contributed weights is designed for targets with different threats to measure the relationship of resource allocation prioritization among multiple targets. Afterward, an optimal distribution model of the joint beam and the dwell time driven by the dynamic threat of the target is established; the utility function is combined with the resources of the netted phased array radar system. Finally, the problem is solved using a reward-based iterative descent search algorithm, and the effectiveness of the algorithm is verified via simulation. The simulation results show that the proposed algorithm can determine the tracking accuracy requirements of different targets and allocate tracking resources based on the multi-target threat assessment results, thereby improving the comprehensive tracking accuracy of networked phased array radars.
- Netted phased array radar /
- Threat assessment /
- Bayesian Cramer-Rao Lower Bound (BCRLB) /
- Multi-Target Tracking (MTT) /
- Resource allocation

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图 1 组网相控阵雷达闭环信息处理流程图

Figure 1. The flowchart of closed-loop information processing in netted phased array radars

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图 2 基于奖励的迭代下降算法程序流程图

Figure 2. The flowchart of the reward-based iterative descending approach

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图 3 目标航迹与雷达节点位置分布图

Figure 3. Deployment of targets with respect to radar nodes

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图 4 目标综合威胁度评估结果

Figure 4. Target threat assessment results

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图 5 各雷达节点波束和驻留时间分配结果

Figure 5. Beam and dwell time allocation results for each radar node

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图 6 各目标持续时间内驻留时间分配结果

Figure 6. The sum of dwell times of targets over all frames

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图 7 采用本文算法的各目标RMSE与BCRLB对比图

Figure 7. Comparison of RMSE and BCRLB using the proposed algorithm

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图 8 采用平均资源分配方法的各目标RMSE与BCRLB对比图

Figure 8. Comparison of RMSE and BCRLB using the average resource allocation method

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表 1 目标初始运动状态及其相对组网雷达中心的运动参数

Table 1. Initial motion states of targets and their motion parameters relative to the center of the netted radar system

目标标号	目标位置(km)	目标速度(km/s)	相对距离(km)	绝对速度(km/s)	航向角(°)
1	(23, 50)	(–0.01, –0.17)	40.11	0.17	0.92
2	(2.5, 40.3)	(0.08, –0.14)	34.99	0.16	0.26
3	(17, 60)	(0.02, –0.34)	50.09	0.34	0.07
4	(30, 53)	(0.12, –0.16)	44.15	0.20	49.96

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