• Electronics Optics & Control
  • Vol. 29, Issue 4, 52 (2022)
ZHU Bakun1, ZHU Weigang2, LI Wei1, YANG Ying1, and GAO Tianhao1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • show less
    DOI: 10.3969/j.issn.1671-637x.2022.04.011 Cite this Article
    ZHU Bakun, ZHU Weigang, LI Wei, YANG Ying, GAO Tianhao. A Review on Reinforcement Learning Based Radar Jamming Decision-Making Technology[J]. Electronics Optics & Control, 2022, 29(4): 52 Copy Citation Text show less
    References

    [3] GONG L LWU S LLV T.A radar emitter identification method based on pulse match template sequence[C]//The 2nd International Conference on Signal Processing Systems.DalianChina:IEEE2010:V3-153-V3-156.

    [5] XIAO Z LYAN Z Y.Radar emitter identification based on feedforward neural networks[C]//IEEE 4th Information TechnologyNetworkingElectronic and Automation Control Conference (ITNEC).ChongqingChina:IEEE 2020:555-558.

    [10] TANG ZGAO X G.Research on the self-defence electronic jamming decision-making based on the discrete dynamic Bayesian network[J].Journal of Systems Engineering and Electronics200819(4):702-708.

    [14] VISNEVSKI NKRISHNAMURTHY VHAYKIN Set al.Multi-function radar emitter modelling:a stochastic discrete event system approach[C]//The 42nd IEEE International Conference on Decision and Control.MauiHI:IEEE2003:6295-6300.

    [15] VISNEVSKI NKRISHNAMURTHY VWANG Aet al.Syntactic modeling and signal processing of multifunction radars:a stochastic context-free grammar approach[J].Proceedings of the IEEE200795(5):1000-1025.

    [25] XING QZHU W GJIA X.Research on method of intelligent radar confrontation based on reinforcement learning[C]//The 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA).BeijingChina:IEEE2017:471-475.

    [26] XING QZHU W GJIA X.Intelligent countermeasure design of radar working-modes unknown[C]//IEEE International Conference on Signal ProcessingCommunications and Computing (ICSPCC).XiamenChina:IEEE2017:1-5.

    [27] YOO JJANG DKIM H Jet al.Hybrid reinforcement learning control for a micro quadrotor flight[J].IEEE Control Systems Letters20205(2):505-510.

    [28] ZHAO Z YWANG QLI X L.Deep reinforcement learning based lane detection and localization[J].Neurocomputing2020413:328-338.

    [29] PARK HSIM M KCHOI D G.An intelligent financial portfolio trading strategy using deep Q-learning[J].Expert Systems with Applications2020158:113573.

    [30] WATKINS C J C HDAYAN P.Technical note:Q-learning[J].Machine Learning19928:279-292.

    [31] SILVER DHUANG AMADDISON C Jet al.Mastering the game of Go with deep neural networks and tree search[J].Nature2016529:484-489.

    [32] VAN HASSELT HGUEZ ASILVER D.Deep reinforcement learning with double Q-learning[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence.PhoenixArizona:AAAI2016:2094-2100.

    ZHU Bakun, ZHU Weigang, LI Wei, YANG Ying, GAO Tianhao. A Review on Reinforcement Learning Based Radar Jamming Decision-Making Technology[J]. Electronics Optics & Control, 2022, 29(4): 52
    Download Citation