|Table of Contents|

Citation:
　Yushan Sun,Wenlong Jiao,Guocheng Zhang,et al.Research on Stealth Assistant Decision System of Submarine Voyage Stage[J].Journal of Marine Science and Application,2020,(2):208-217.[doi:10.1007/s11804-020-00143-5]
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Research on Stealth Assistant Decision System of Submarine Voyage Stage

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Title:
Research on Stealth Assistant Decision System of Submarine Voyage Stage
Author(s):
Yushan Sun1;  Wenlong Jiao2;  Guocheng Zhang1;  Lifeng Wang3;  Junhan Cheng1
Affilations:
Author(s):
Yushan Sun1;  Wenlong Jiao2;  Guocheng Zhang1;  Lifeng Wang3;  Junhan Cheng1
1 Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, China;
2 The 36 th Research Institute of China Electronics Technology Group Corporation, Jiaxing 314000, China;
3 Marine Design & Research Institute of China, Shanghai 200011, China
Keywords:
Submarine; Dynamic stealth; Assistant decision; Fuzzy neural network; Improved simplified particle swarm optimization
分类号:
-
DOI:
10.1007/s11804-020-00143-5
Abstract:
Stealth security has always been considered as an important guarantee for the vitality and combat effectiveness of submarines. In accordance with the stealth requirements of submarines performing stealth voyage tasks, this paper proposes a stealth assistant decision system. Firstly, the submarine stealth posture is acquired. A fuzzy neural network inference engine based on improved simplified particle swarm optimization is designed. The auxiliary decision-making scheme for state control and maneuver avoidance of submarine and its equipment is automatically generated. Secondly, the simulation and deduction of the assistant decision-making scheme are realized by the calculation modules of sound source level, propagation loss, and stealth situation. The assistant decision-making scheme and simulation result provide decision support for the commander. Thirdly, the simulation experiment platform of the submarine stealth assistant decision system is constructed. The submarine stealth assistant decision system described in this paper can quickly and efficiently produce assistant decision-making schemes, including submarine and equipment control and maneuver avoidance. The scheme is in line with the combat experience and the results of the pre-model simulation experiments, whereas the simulation deduction evaluates the rationality and effectiveness of the selected scheme. The submarine stealth assistant decision system can adapt to a complex battlefield environment in addition to rapidly and accurately providing assistance in decision-making.

References:

Ainslie M (2010) Principles of sonar performance modelling. Springer, Berlin. https://doi.org/10.1007/978-3-540-87662-5
Chwaszczewski RS, Slater Michael, Snyder Jennifer, Tietje E, Green Robert (2009) Reinventing submarine signature measurements: Installation of the High Gain Measurement System at SEAFAC. 1-6. https://doi.org/10.23919/OCEANS.2009.5422077
Esteban LG, Palacios PD, Conde M, Fernández FG, García-Iruela A, González-Alonso M (2017) Application of artificial neural networks as a predictive method to differentiate the wood of pinus sylvestris, l. and pinus nigra, arn subsp. salzmannii, (dunal) franco. Wood Sci Technol 51(5):1249-1258. https://doi.org/10.1007/s00226-017-0932-7
Eugene M (2007) Can Russian strategic submarines survive at sea? The fundamental limits of passive acoustics. Sci Glob Secur 4(2):213-251. https://doi.org/10.1080/08929889408426401
Fewell MP, Thredgold J, Kershaw DJ (2008) Benefits of sharing detections for networked track initiation in anti-submarine warfare. Edinburgh, S. Aust: Maritime Operations Division, Defence Science and Technology Organisation. http://pandora.nla.gov.au/pan/24592/20080825-1003/DSTO-TR-2086%20PR.pdf
He T, Sun YD, Wu WW, Wu YS (2015) Low noise collocation on fluid pipeline system. J Ship Mech 19(9):1149-1159. https://doi.org/10.3969/j.issn.1007-7294.2015.09.011
Hu W, Li ZS (2007) A simpler and more effective particle swarm optimization algorithm. Journal of Software 18(4):861-868. CNKI:SUN:RJXB.0.2007-04-008
Jian WU, Dai J, Zhou J, Yuhua YU (2011) Active avoidance technique by stealth course planning in complicate battlefield environment. J Projectiles Rocket Missiles Guid 31(4):172-171. https://doi.org/10.15892/j.cnki.djzdxb.2011.04.025
Kui DU, Cheng GL (2010) Method of submarine noise monitoring based on on-board equipment. Ordnance Industry Automation 2:5-7 CNKI:SUN:BGZD.0.2010-02-004
Lee CY (2010) Non-linear system control using a recurrent fuzzy neural network based on improved particle swarm optimisation. Taylor & Francis, Inc, Milton Park, pp 381-395. https://doi.org/10.1080/00207720903045783
Li SZ (2012) The research of assessment model for network learning based on adaptive network-based fuzzy inference system. Comput Technol Autom 31(2):129-132. https://doi.org/10.3969/j.issn.1003-6199.2012.02.030
Liao SH (2000) Case-based decision support system: architecture for simulating military command and control. Eur J Oper Res 123(3):558-567. https://doi.org/10.1016/s0377-2217(99)00109-5
Liu YJ, Jiang BB, Du L, Chen L (2016) The application of expert system based on FNN in decision on the optimum crop growth environment. J Shenyang Ligong Univ 35:26. https://doi.org/10.3969/j.issn.1003-1251.2016.03.005
Ovalle D, García-Pelaez J (2016) Minimizing underwater noise generated by submarine maneuvering: an optimal control approach. IEEE J Ocean Eng 41(2):362-372. https://doi.org/10.1109/joe.2015.2444531
Pan X, Yan T, Juniper R (2008) Active control of radiated pressure of a submarine hull. J Sound Vib 311(1-2):224-242. https://doi.org/10.1016/j.jsv.2007.09.001
Qiang SU, Wang GB, Zhu PF, Song Y (2014) Summarize of foreign submarine acoustic stealth frontier technologies development. Ship Sci Technol 1:1-9. https://doi.org/10.3404/j.issn.1672-7649.2014.01.001
Sun F, Gao F, Pan C, Shengquan LI, Wang B (2014) Comparative analysis of Marsh-Schulkin and Rogers model. Hydrogr Surv Charting 34(3):44-47. https://doi.org/10.3969/j.issn.1671-3044.2014.03.012
Sun XH, Da LL, Li YY (2015) Research on underwater 3d acoustics energy field simulation and visualization method. Ship Sci Technol 9:120-124 CNKI:SUN:JCKX.0.2015-09-024
Tang Z (2007) Study on method of evaluating acoustic stealth situation of submarine. J Wuhan Univ Technol 31(1):17-20. https://doi.org/10.3963/j.issn.2095-3844.2007.01.005
Vostakolaee ES, Amirshahi B (2016) Designing a DSS to explore cement mines using fuzzy neural networks. International Congress on Technology. IEEE.
Wang Y (2010) A present situation and development of noise control for submarines abroad. Ship Electron Eng 1:7-10 CNKI:SUN:JCGC.0.2010-01-003
Yang M, Yang SL, Xu X, Ju RS, Huang KD (2016) Framework of operational analytic simulation in high level decision-making system. Syst Eng Electron 6:1440-1449. https://doi.org/10.3969/j.issn.1001-506X.2016.06.34
Zhang LL, Yang RJ, Gao XQ (2012) Research on submarine localization and evasion on sonobuoys array. Fire Control Command Control 37(5):27-30. https://doi.org/10.1007/s11783-011-0280-z
Zhou D, Nan JC, Gao MM (2015) Fuzzy neural network for modeling based on improved simplified particle swarm optimization. Appl Res Comput 32(4). https://doi.org/10.3969/j.issn.1001-3695.2015.04.009

Memo

Memo:
Received date:2018-07-02;Accepted date:2019-02-01。
Foundation item:National Natural Science Foundation of China (51709061,51779057).
Corresponding author:Guocheng Zhang,zhang_china2018@163.com
Last Update: 2020-11-07