In:
Electronics, MDPI AG, Vol. 11, No. 19 ( 2022-09-28), p. 3097-
Abstract:
The autonomous technology of unmanned platforms is the most dynamic frontier among fields of technology and, inevitably, is trending towards future development. Aiming at the dual requirements of reliable and real-time autonomous decision-making of unmanned underwater vehicles in complex and unfamiliar environments, this article proposes an intelligent decision-making method of attack behavior based on model fusion. The experimental dataset is generated through simulation modeling, and an appropriate amount of noise is added to simulate the observation error in a real situation. The threshold of weapon-hit probability is set according to the requirements of combat missions, and the decision-making of attack behavior is transformed into the problem of imbalanced sample classification with noisy data. Through theoretical analysis and experimental testing, the classification effects of algorithms such as Logistic Regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), and ensemble learning are compared. On this basis, the intelligent decision model is constructed by using synthetic minority oversampling technique resampling and three model fusion methods of voting, stacking, and blending. The experimental results show that compared with traditional simulation decision-making and common classification algorithms, the proposed method has higher accuracy, recall rate, area-under-the-curve value, and model generalization ability. It can not only effectively identify the impact of noise data on attack-behavior decision-making, but also ensures the decision-making speed through offline training, and provides references for the research in the field of equipment development and intelligent decision-making in the future.
Type of Medium:
Online Resource
ISSN:
2079-9292
DOI:
10.3390/electronics11193097
Language:
English
Publisher:
MDPI AG
Publication Date:
2022
detail.hit.zdb_id:
2662127-7
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