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.
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National Natural Science Foundation of China (51709061,51779057).
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Article Highlights
• The stealth assistant decision-making system including the stealth assistant decision-making module and simulation module is constructed.
• Based on the fuzzy neural network, the inference engine of the stealth assistant decision-making scheme is established, which can adapt to the generation of multi input and multi output nonlinear stealth assistant decision-making scheme.
• Based on the improved simplified particle swarm optimization algorithm, the learning of a fuzzy neural network is realized, the optimization ability of particle swarm optimization is enhanced, and the convergence speed and training efficiency are effectively improved.
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Sun, Y., Jiao, W., Zhang, G. et al. Research on Stealth Assistant Decision System of Submarine Voyage Stage. J. Marine. Sci. Appl. 19, 208–217 (2020). https://doi.org/10.1007/s11804-020-00143-5
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DOI: https://doi.org/10.1007/s11804-020-00143-5