An Application of Artificial Neural Networks to Estimate the Performance of High-Energy Laser Weapons in Maritime Environments

被引:6
作者
Lionis, Antonios [1 ]
Tsigopoulos, Andreas [2 ]
Cohn, Keith [3 ]
机构
[1] Univ Peloponnese, Informat & Telecommun Dept, Tripoli 22100, Greece
[2] Hellen Naval Acad, Div Combat Syst, Naval Operat Sea Sci Nav Elect & Telecommun Sect, Piraeus 18538, Greece
[3] Naval Postgrad Sch, Phys Dept, Monterey, CA 93943 USA
关键词
high-energy laser; artificial neural networks; Naval Postgraduate School; laser performance; atmospheric propagation modeling; atmospheric turbulence; TURBULENCE;
D O I
10.3390/technologies10030071
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Efforts to develop high-energy laser (HEL) weapons that are capable of being integrated and operated aboard naval platforms have gained an increased interest, partially due to the proliferation of various kinds of unmanned systems that pose a critical asymmetric threat to them, both operationally and financially. HEL weapons allow for an unconstrained depth of magazine and cost exchange ratio, both of which are essential characteristics to effectively oppose small unmanned systems, compared to their kinetic weapons counterparts. However, HEL performance is heavily affected by atmospheric conditions between the weapon and the target; therefore, the more precise and accurate the atmospheric characterization, the more accurate the performance estimation of the HEL weapon. To that end, the Directed Energy Group of the Naval Postgraduate School (NPS) is conducting experimental, theoretical and computational research on the effects of atmospheric conditions on HEL weapon efficacy. This paper proposes a new approach to the NPS laser performance code scheme, which leverages artificial neural networks (ANNs) for the prediction of optical turbulence strength. This improvement could allow for near real-time and location-independent HEL weapon performance estimation. Two experimental datasets, which were obtained from the NPS facilities, were utilized to perform regression modeling using an ANN, which achieved a decent fit (R-2 = 0.75 for the first dataset and R-2 = 0.78 for the second dataset).
引用
收藏
页数:10
相关论文
共 27 条
  • [1] Andrews L. C., 2001, LASER BEAM SCINTILLA
  • [2] Ellis J.D., 2015, DIRECTED-ENERGY WEAPONS : Promise and Prospects
  • [3] Fiorino S, 2019, THESIS NAVAL POSTGRA
  • [4] Frederickson P., 2019, THESIS NAVAL POSTGRA
  • [5] Measurements and modeling of optical turbulence in a maritime environment
    Frederickson, Paul A.
    Hammel, Stephen
    Tsintikidis, Dimitris
    [J]. ATMOSPHERIC OPTICAL MODELING, MEASUREMENT, AND SIMULATION II, 2006, 6303
  • [6] Gildemeyer S.J., 2018, ANAL SHIPBOARD EFFEC
  • [7] Machine-learning informed macro-meteorological models for the near-maritime environment
    Jellen, Christopher
    Oakley, Miles
    Nelson, Charles
    Burkhardt, John
    Brownell, Cody
    [J]. APPLIED OPTICS, 2021, 60 (11) : 2938 - 2951
  • [8] Machine learning informed predictor importance measures of environmental parameters in maritime optical turbulence
    Jellen, Christopher
    Burkhardt, John
    Brownell, Cody
    Nelson, Charles
    [J]. APPLIED OPTICS, 2020, 59 (21) : 6379 - 6389
  • [9] Kaushal H, 2017, OPT NETW, P1, DOI 10.1007/978-81-322-3691-7
  • [10] Kui J.R, 2019, THESIS NAVAL POSTGRA