自适应预测权重的空战鲁棒机动决策方法

doi:10.3969/j.issn.1001-506X.2020.10.16

Abstract

Abstract:

Aiming at the problem of close range air combat with drastic changes in air combat situation, a robust maneuvering decision-making optimization method with adaptive state prediction weight adjustment mechanism is proposed. Firstly, in order to make the unmanned combat air vehicle (UCAV) robust to the fluctuation of situation parameters, a robust situation function is designed to represent the air combat situation. Then, aiming at the uncertainty of target maneuvering, the reachable set theory is used to predict the maneuvering intention and state of the target in advance, and the adaptive prediction weight coefficient is used to adjust the attack and defense of the UCAV. Finally, the improved symbiotic biological optimization algorithm is used to optimize the maneuvering decision control variables. The simulation results show that the UCAV can gene-rate an ideal close range attack occupation trajectory by using this method, and realize the close range autonomous air combat attack occupation.

Key words: robust situation function, reachable set theory, maneuvering decision-making, adaptive prediction weight coefficient

CLC Number:

V279

Dali DING, Zhenglei WEI, Shangqin TANG, Changqiang HUANG. Robust maneuvering decision-making method for air combat using adaptive prediction weight[J]. Systems Engineering and Electronics, 2020, 42(10): 2275-2284.

Figures/Tables 17

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Robust maneuvering decision-making method for air combat using adaptive prediction weight

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