体系作战下巡航导弹的动态隐身

doi:10.12305/j.issn.1001-506X.2022.09.16

Abstract

Abstract:

Through the system combat simulation platform under system combat, this paper uses the reinforcement learning method to adjust the cruise missile trajectory in mission to achieve dynamic stealth facing the enemy's sensors. Taking cruise missiles to penetrate the US military's "Navy Integrated Fire Control and Control Air" system as a typical combat scenario, based on the system combat simulation platform, a reward function is designed by including dynamic early warning threats and intercept misses, and the deep deterministic policy gradient algorithm is used to train the cruise missile penetration model. After the model training is completed, cruise missiles can perceive the state of early warning aircraft and intercepting missiles, and change their maneuvering trajectories to reduce detection probability and increase interception misses to achieve dynamic stealth effect, and then enhance its battlefield survivability and penetration ability.

Key words: cruise missile, deep deterministic policy gradient algorithm, radar stealth, penetration strategy

CLC Number:

TJ76

Zijie MA, Yongjun XIE. Dynamic stealth of cruise missile in system combat[J]. Systems Engineering and Electronics, 2022, 44(9): 2826-2831.

Figures/Tables 9

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Dynamic stealth of cruise missile in system combat

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