反鱼雷鱼雷作战效能智能化预估方法

An Intelligent Prediction Method of Anti-torpedo Torpedo Combat Effectiveness

随着深度学习技术的不断发展,利用智能化技术辅助作战和军事应用已成为目前研究的新热点。论文针对反鱼雷鱼雷的作战效能预估问题,首先,设计了反鱼雷鱼雷全弹道数字仿真系统平台,构建了反鱼雷鱼雷防御作战的大样本数据集;其次,以鱼雷报警声纳可探测的评估指标、反鱼雷鱼雷的打击方式为基础,提取反鱼雷鱼雷作战效能影响要素;最后,构建了深度神经网络预测模型和贝叶斯网络预测模型两种智能化方法,并验证了其作战效能预测的准确率。

With the continuous development of deep learning,the use of intelligent technology to assist combat and military applications has become a new research hotspot.In this paper,aiming at the combat effectiveness of anti-torpedo torpedo estimation problem,first of all,the digital simulation system platform of anti-torpedo torpedo trajectory is designed,and the large sample data set of anti-torpedo torpedo defense operation is constructed.Secondly,the factors affecting the effectiveness of the anti-torpedo operation are extracted based on the evaluation index of the detection of torpedo alarm sonar and the attack mode of the anti-torpedo.Finally,two intelligent methods,deep neural network prediction model and Bayesian network prediction model,are constructed,and the accuracy of combat effectiveness prediction is verified.