Volume 12 Issue 3
Jun.  2023
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Article Navigation >  Journal of Radars  > 2023  >  12(3): 616-628
CHENG Ting, HENG Siyu, and LI Zhongzhu. Real-time dwell scheduling algorithm for distributed phased array radar network based on pulse interleaving[J]. Journal of Radars, 2023, 12(3): 616–628. doi: 10.12000/JR22211
Citation: CHENG Ting, HENG Siyu, and LI Zhongzhu. Real-time dwell scheduling algorithm for distributed phased array radar network based on pulse interleaving[J]. Journal of Radars, 2023, 12(3): 616–628. doi: 10.12000/JR22211
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Real-time Dwell Scheduling Algorithm for Distributed Phased Array Radar Network Based on Pulse Interleaving

DOI: 10.12000/JR22211

  • 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 (61771095, 62031007)
More Information
  • Corresponding author: CHENG Ting, citrus@uestc.edu.cn
  • Received Date: 2022-10-27
  • Rev Recd Date: 2022-12-12
    • Available Online: 2022-12-13
  • Publish Date: 2022-12-17
    Abstract
  • In this study, a real-time dwell scheduling algorithm based on pulse interleaving is proposed for a distributed radar network system. A time pointer vector is introduced to indicate the moment when the dwell task with the highest synthetic priority should be chosen. This task is further allocated to the radar node with the lowest interleaving time utilization ratio, effectively reducing the time gaps during scheduling. Meanwhile, the pulse interleaving analysis determines whether the assigned dwell task can be scheduled successfully on the corresponding radar node. The time slot occupation matrix and energy assumption matrix are introduced to indicate the time and energy resource consumption of radar nodes, which not only simplifies the pulse interleaving analysis process but also enables pulse interleaving among the tasks with different pulse repetition intervals and numbers. Furthermore, to improve the efficiency of dwell scheduling, a threshold of interleaving time utilization ratio is set to adaptively choose the sliding step of the time pointer. The simulation results reveal that the proposed algorithm can execute real-time dwell scheduling for a distributed radar network system and achieve better scheduling performance than the existing dwell scheduling algorithm.

     

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    • References(29)
  • [1]
    周文辉. 相控阵雷达及组网跟踪系统资源管理技术研究[D]. [博士论文], 国防科学技术大学, 2004.

    ZHOU Wenhui. Research on resource management technology for phased array radar and its network in tracking system[D]. [Ph. D. dissertation], University of Defense Technology, 2004.
    [2]
    YUAN Ye, YI Wei, HOSEINNEZHAD R, et al. Robust power allocation for resource-aware multi-target tracking with colocated MIMO radars[J]. IEEE Transactions on Signal Processing, 2021, 69: 443–458. doi: 10.1109/TSP.2020.3047519
    [3]
    YUAN Ye, YI Wei, KIRUBARAJAN T, et al. Scaled accuracy based power allocation for multi-target tracking with colocated MIMO radars[J]. Signal Processing, 2019, 158: 227–240. doi: 10.1016/j.sigpro.2019.01.014
    [4]
    卢建斌, 胡卫东, 郁文贤. 多功能相控阵雷达实时任务调度研究[J]. 电子学报, 2006, 34(4): 732–736. doi: 10.3321/j.issn:0372-2112.2006.04.032

    LU Jianbin, HU Weidong, and YU Wenxian. Study on real-time task scheduling of multifunction phased array radars[J]. Acta Electronica Sinica, 2006, 34(4): 732–736. doi: 10.3321/j.issn:0372-2112.2006.04.032
    [5]
    ZHANG Haowei, XIE Junwei, ZONG Binfeng, et al. Dynamic priority scheduling method for the air-defence phased array radar[J]. IET Radar, Sonar & Navigation, 2017, 11(7): 1140–1146. doi: 10.1049/iet-rsn.2016.0549
    [6]
    QU Zhen, DING Zhen, and MOO P. Dual-side scheduling for radar resource management[C]. 21st International Radar Symposium, Warsaw, Poland, 2020: 260–263.
    [7]
    CHENG Ting, HE Zishu, and TANG Ting. Dwell scheduling algorithm for multifunction phased array radars based on the scheduling gain[J]. Journal of Systems Engineering and Electronics, 2008, 19(3): 479–485. doi: 10.1016/S1004-4132(08)60110-3
    [8]
    CHEN Yijun, ZHANG Qun, YUAN Ning, et al. An adaptive ISAR-imaging-considered task scheduling algorithm for multi-function phased array radars[J]. IEEE Transactions on Signal Processing, 2015, 63(19): 5096–5110. doi: 10.1109/TSP.2015.2449251
    [9]
    段毅, 谭贤四, 曲智国, 等. 基于可变时间窗的相控阵雷达事件调度方法[J]. 现代雷达, 2018, 40(2): 1–6. doi: 10.16592/j.cnki.1004-7859.2018.02.001

    DUAN Yi, TAN Xiansi, QU Zhiguo, et al. Phased array radar scheduling method based on variable time window[J]. Modern Radar, 2018, 40(2): 1–6. doi: 10.16592/j.cnki.1004-7859.2018.02.001
    [10]
    TAN Qianqian, CHENG Ting, and LI Xi. Online adaptive dwell scheduling based on dynamic template for PAR[J]. Journal of Systems Engineering and Electronics, 2021, 32(5): 1119–1129. doi: 10.23919/JSEE.2021.000096
    [11]
    CHENG Ting, LI Zhongzhu, TAN Qianqian, et al. Real-time adaptive dwell scheduling for digital array radar based on virtual dynamic template[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(4): 3197–3208. doi: 10.1109/TAES.2022.3145773
    [12]
    YANG Shanchao, TIAN Kangsheng, and LIU Renzheng. Task scheduling algorithm based on value optimisation for anti-missile phased array radar[J]. IET Radar, Sonar & Navigation, 2019, 13(11): 1883–1889. doi: 10.1049/iet-rsn.2019.0163
    [13]
    MENG Fanqing and TIAN Kangsheng. Phased-array radar task scheduling method for hypersonic-glide vehicles[J]. IEEE Access, 2020, 8: 221288–221298. doi: 10.1109/ACCESS.2020.3043338
    [14]
    ABDELAZIZ F B and MIR H. An optimization model and tabu search heuristic for scheduling of tasks on a radar sensor[J]. IEEE Sensors Journal, 2016, 16(17): 6694–6702. doi: 10.1109/JSEN.2016.2587730
    [15]
    QU Zhen, DING Zhen, and MOO P. A machine learning radar scheduling method based on the EST algorithm[C]. IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing, Milan, Italy, 2019: 22–27.
    [16]
    QU Zhen, DING Zhen, and MOO P. A machine learning task selection method for radar resource management (poster)[C]. 22th International Conference on Information Fusion, Ottawa, Canada, 2019: 1–6.
    [17]
    ZHANG Haowei, XIE Junwei, GE Jiaang, et al. A hybrid adaptively genetic algorithm for task scheduling problem in the phased array radar[J]. European Journal of Operational Research, 2019, 272(3): 868–878. doi: 10.1016/j.ejor.2018.07.012
    [18]
    ZHANG Haowei, XIE Junwei, GE Jiaang, et al. An entropy-based PSO for DAR task scheduling problem[J]. Applied Soft Computing, 2018, 73: 862–873. doi: 10.1016/j.asoc.2018.09.022
    [19]
    ZHANG Haowei, XIE Junwei, HU Qiyong, et al. A hybrid DPSO with levy flight for scheduling MIMO radar tasks[J]. Applied Soft Computing, 2018, 71: 242–254. doi: 10.1016/j.asoc.2018.06.028
    [20]
    SHAGHAGHI M and ADVE R S. Task selection and scheduling in multifunction multichannel radars[C]. IEEE Radar Conference, Seattle, USA, 2017: 969–974.
    [21]
    SHAGHAGHI M and ADVE R S. Machine learning based cognitive radar resource management[C]. IEEE Radar Conference, Oklahoma City, USA, 2018: 1433–1438.
    [22]
    SHAGHAGHI M, ADVE R S, and DING Zhen. Multifunction cognitive radar task scheduling using Monte Carlo tree search and policy networks[J]. IET Radar, Sonar & Navigation, 2018, 12(12): 1437–1447. doi: 10.1049/iet-rsn.2018.5276
    [23]
    TIAN Tuanwei, ZHANG Tianxian, and KONG Lingjiang. Timeliness constrained task scheduling for multifunction radar network[J]. IEEE Sensors Journal, 2019, 19(2): 525–534. doi: 10.1109/JSEN.2018.2878795
    [24]
    LI Xueting, XU Longxiao, ZHANG Tianxian, et al. A scheduling method of generalized tasks for multifunctional radar network[C]. International Conference on Control, Automation and Information Sciences, Chengdu, China, 2019: 1–6.
    [25]
    XU Longxiao, ZHANG Tianxian, WANG Yuanhang, et al. Resources conversion and complementarity based tasks scheduling for multifunction radar network[C]. IEEE Radar Conference, Boston, USA, 2019: 1–5.
    [26]
    XU Longxiao and ZHANG Tianxian. Reinforcement learning based dynamic task scheduling for multifunction radar network[C]. IEEE Radar Conference, Florence, Italy, 2020: 1–5.
    [27]
    LIU Xiaowen, ZHANG Qun, CHEN Yichang, et al. Task allocation optimization for multi-target ISAR imaging in radar network[J]. IEEE Sensors Journal, 2018, 18(1): 122–132. doi: 10.1109/JSEN.2017.2771804
    [28]
    LIU Xiaowen, ZHANG Qun, LUO Ying, et al. ISAR imaging task allocation for multi-target in radar network based on potential game[J]. IEEE Sensors Journal, 2019, 19(23): 11192–11204. doi: 10.1109/JSEN.2019.2936423
    [29]
    LIU Xiaowen, ZHANG Qun, LUO Ying, et al. Radar network time scheduling for multi-target ISAR task with game theory and multiagent reinforcement learning[J]. IEEE Sensors Journal, 2021, 21(4): 4462–4473. doi: 10.1109/JSEN.2020.3029430
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