Adaptive Recognition Method of Capability Boundary Parameters for Unmanned Autonomous Systems

doi:10.16182/j.issn1004731x.joss.23-0775

Abstract

Abstract:

To effectively cope with the dimension curse in simulation testing and reduce the number of simulations times needed in the traditional full-space parameter traversal, it is necessary to obtain specific simulation data to accurately reflect the modeling characteristics of the test data to obtain the informative and representative samples of the original data with a smaller number of simulations. A digital simulation test model for adaptive recognition;/of capability boundary parameters for UAS is proposed. The model is initially constructed with a good point set with a multi-weight structure; In combination with an adaptive kernel function boundary point recognition, the model is iteratively optimized by Gaussian process regression, so as to adaptively detect the capability boundary of UAS. The experimental results show that the method can reduce the amount of data required for modeling and improve the efficiency of adaptive parameter boundary recognition, which provides an approach to enhance the efficiency of intelligent UAS testing.

Key words: unmanned autonomous system, adaptive recognition of boundary parameters, gaussian process regression model, adaptive kernel function, good points set, simulation testing

CLC Number:

TP391.9

Li Jinwen, Wang Peng, Pan Youmei, Hui Xinyao. Adaptive Recognition Method of Capability Boundary Parameters for Unmanned Autonomous Systems[J]. Journal of System Simulation, 2024, 36(10): 2359-2370.

Figures/Tables 8

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Table 1

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数据集名称	规模(点个数)	簇数	对应文献
Aggregation	788	7	[35-39]
Flame	240	2	[40-42]
Two⁃moon	400	2	[43-46]

参数	Aggregation	Flame	Two moon
MSE	初始模型： 8.33×10^-4	初始模型：4.69×10^-3	初始模型：3.26×10^-3
MSE	最终模型： 6.24×10^-4	最终模型：3.94×10^-3	最终模型：4.93×10^-4
MAE	初始模型： 3.47×10^-1	初始模型：6.55×10^-1	初始模型：4.93×10^-1
MAE	最终模型： 3.34×10^-1	最终模型：6.53×10^-1	最终模型：3.05×10^-1
MedianAE	初始模型： 2.17×10^-1	初始模型：4.94×10^-1	初始模型：3.97×10^-1
MedianAE	最终模型： 2.15×10^-1	最终模型：3.65×10^-1	最终模型：1.28×10^-1

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