| Citation: | XING H X，XING Q H. An optimal scheduling model for scintillation detection of netted radars[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（12）：3884-3893 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0924
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When the jammer executes the ground self-screening jamming and penetration action, the scintillation detection system of air-defense netted radars can guarantee the efficiency of cooperative detection of air-defense radars and improve the concealment of the system. An optimal scheduling model for scintillation detection of air-defense netted radars based on a multi-objective artificial bee colony (MOABC) algorithm was proposed. Firstly, the azimuth-range interval was processed, and the radar network detection responsibility area was divided into multiple grids. The jammer track was discretized, and the grid center point was approximated to the position of the jammer in different time slots. Then, the optimal scheduling model for scintillation detection of radar network was established, which took the threat degree of jammer, active detection time, and detection probability as optimization objectives. Finally, the MOABC algorithm was used to solve the radar scheduling scheme in a working state, and decimal integer encoding was adopted to reduce the search space dimension. The simulation results show that the optimized scheduling scheme can significantly improve the survivability of ground radar positions compared with other strategies and ensure the detection ability of radar networks.
| [1] |
YANG Z P, YANG S N, ZHOU Q S, et al. A joint optimization algorithm for focused energy delivery in precision electronic warfare[J]. Defence Technology, 2022, 18(4): 709-721. doi: 10.1016/j.dt.2021.03.001
|
| [2] |
GENG Z. Evolution of netted radar systems[J]. IEEE Access, 2020, 8: 124961-124977. doi: 10.1109/ACCESS.2020.3007774
|
| [3] |
WU Z J, WANG F, ZHOU J J. Netted radar tracking with multiple simultaneous transmissions against combined PDS interception[J]. Journal of Sensors, 2020, 2020: 5932539.
|
| [4] |
OECHSLIN R, WIELAND S, ZUTTER A, et al. Fully adaptive resource management in radar networks[C]//Proceedings of the IEEE Radar Conference. Piscataway: IEEE Press, 2020: 1-6.
|
| [5] |
孙兵, 李龙骧, 罗景青. 协同侦察系统增加猝发探测功能的定位技术[J]. 航天电子对抗, 2016, 32(3): 9-12. doi: 10.3969/j.issn.1673-2421.2016.03.003
SUN B, LI L X, LUO J Q. Location technology of synergy reconnaissance system adding instantaneous detection function[J]. Aerospace Electronic Warfare, 2016, 32(3): 9-12 (in Chinese). doi: 10.3969/j.issn.1673-2421.2016.03.003
|
| [6] |
高石印, 石玮, 王钦, 等. 地对空雷达干扰机布阵与开机时序控制研究[J]. 空军预警学院学报, 2020, 34(5): 346-350.
GAO S Y, SHI W, WANG Q, et al. Research on ground-to-air radar jammer embattling and jamming time sequence control[J]. Journal of Air Force Early Warning Academy, 2020, 34(5): 346-350 (in Chinese).
|
| [7] |
陈兴凯, 韩壮志, 封吉平, 等. 基于跟踪精度的火控雷达网间歇开机控制策略[J]. 探测与控制学报, 2013, 35(5): 74-78.
CHEN X K, HAN Z Z, FENG J P, et al. Intermittent control strategy of fire-control radar network based on tracking accuracy[J]. Journal of Detection & Control, 2013, 35(5): 74-78 (in Chinese).
|
| [8] |
YAN J K, JIAO H, PU W Q, et al. Radar sensor network resource allocation for fused target tracking: A brief review[J]. Information Fusion, 2022, 86-87: 104-115. doi: 10.1016/j.inffus.2022.06.009
|
| [9] |
USTUN D, TOKTAS A, ERKAN U, et al. Modified artificial bee colony algorithm with differential evolution to enhance precision and convergence performance[J]. Expert Systems with Applications, 2022, 198: 116930. doi: 10.1016/j.eswa.2022.116930
|
| [10] |
YE T Y, WANG W J, WANG H, et al. Artificial bee colony algorithm with efficient search strategy based on random neighborhood structure[J]. Knowledge-Based Systems, 2022, 241: 108306. doi: 10.1016/j.knosys.2022.108306
|
| [11] |
舒文江. 分布式雷达智能调度中的辅助决策技术研究[D]. 成都: 电子科技大学, 2020.
SHU W J. Research on assistant decision-making technology in intelligent scheduling of distributed radar[D]. Chengdu: University of Electronic Science and Technology of China, 2020 (in Chinese).
|
| [12] |
李波, 李卿莹, 高晓光, 等. 基于序优化的多传感器协同雷达辐射控制[J]. 系统工程与电子技术, 2018, 40(7): 1465-1471. doi: 10.3969/j.issn.1001-506X.2018.07.08
LI B, LI Q Y, GAO X G, et al. Radar radiation control under multiple sensor synergy based on ordinal optimization[J]. Systems Engineering and Electronics, 2018, 40(7): 1465-1471 (in Chinese). doi: 10.3969/j.issn.1001-506X.2018.07.08
|
| [13] |
FENG J F, ZHANG Q, HU J H, et al. Dynamic assessment method of air target threat based on improved GIFSS[J]. Journal of Systems Engineering and Electronics, 2019, 30(3): 525-534. doi: 10.21629/JSEE.2019.03.10
|
| [14] |
郭佳. 基于多属性决策的空中目标威胁评估方法研究[D]. 北京: 北京理工大学, 2017.
GUO J. Research on threat assessment method of air target based on multi-attribute decision making[D]. Beijing: Beijing Institute of Technology, 2017 (in Chinese).
|
| [15] |
CHEN Y F, WU Y, CHEN N, et al. New approximate distributions for the generalized likelihood ratio test detection in passive radar[J]. IEEE Signal Processing Letters, 2019, 26(5): 685-689. doi: 10.1109/LSP.2019.2903632
|
| [16] |
RICHTER R, GOMES N A S. A-4 skyhawk aircraft stealth capacity against L-band radar based on dynamic target detection[C]//Proceedings of the IEEE Radar Conference. Piscataway: IEEE Press, 2020: 1-5.
|
| [17] |
李光明, 唐业敏, 蒋苏蓉. 雷达网反隐身性能评估—雷达网综合发现概率[J]. 现代雷达, 2006(1): 23-25. doi: 10.3969/j.issn.1004-7859.2006.01.007
LI G M, TANG Y M, JIANG S R. Performance evaluation of radar network for counterchecking stealth aircraft comprehensive detecting probability of radar network[J]. Modern Radar, 2006(1): 23-25 (in Chinese). doi: 10.3969/j.issn.1004-7859.2006.01.007
|
| [18] |
王旭, 宋笔锋, 郭晓辉. 飞行器被雷达发现概率的计算方法研究[J]. 系统工程理论与实践, 2006, 26(6): 130-134. doi: 10.3321/j.issn:1000-6788.2006.06.022
WANG X, SONG B F, GUO X H. Research on the approach for calculating the probability of detecting an aircraft by radar system[J]. Systems Engineering-Theory & Practice, 2006, 26(6): 130-134 (in Chinese). doi: 10.3321/j.issn:1000-6788.2006.06.022
|
| [19] |
NIYOMUBYEYI O, SICUAIO T E, DÍAZ GONZÁLEZ J I, et al. A comparative study of four metaheuristic algorithms, AMOSA, MOABC, MSPSO, and NSGA-II for evacuation planning[J]. Algorithms, 2020, 13(1): 16. doi: 10.3390/a13010016
|
| [20] |
CHANG T Q, KONG D P, HAO N, et al. Solving the dynamic weapon target assignment problem by an improved artificial bee colony algorithm with heuristic factor initialization[J]. Applied Soft Computing, 2018, 70: 845-863. doi: 10.1016/j.asoc.2018.06.014
|
| [21] |
MA L B, WANG X W, HUANG M, et al. Two-level master–slave RFID networks planning via hybrid multiobjective artificial bee colony optimizer[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49(5): 861-880. doi: 10.1109/TSMC.2017.2723483
|
| [22] |
LI Y, KOU Y X, LI Z W, et al. A modified Pareto ant colony optimization approach to solve biobjective weapon-target assignment problem[J]. International Journal of Aerospace Engineering, 2017, 2017: 1746124.
|
| [23] |
唐嘉诚. 多功能雷达组网资源调度方法研究[D]. 成都: 电子科技大学, 2021.
TANG J C. Research on resource scheduling method of multifunctional radar networking[D]. Chengdu: University of Electronic Science and Technology of China, 2021(in Chinese).
|
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