摘要
针对不平衡难分类条件下空中目标群组意图快速识别的难题,提出一种基于滑动窗口估计的时空卷积自注意力网络模型的意图识别方法。该方法根据特征数据的特点对其使用滑动窗口的预先处理,通过时空卷积网络快速提取多维时序特征数据的流信息;然后采用自注意力机制捕捉每个特征数据的关键特征并优化权重。仿真结果表明该方法有效提升了不平衡样本中难分类样本意图识别的训练效率和分类的准确率。
Aimed at the problem that air target group intent is often difficult to be identified rapidly under condition of imbalanced and difficult classification,an intent recognition method is proposed based on moving-window estimation of the temporal convolution self-attention network model.First,the proposed model is intended to preprocess the feature data by the moving-window estimation method.Second,the flow information of multi-dimensional time series feature data is quickly extracted by the temporal convolution network(TCN).Finally,the self-attention mechanism is used to capture the key features from each feature datum and optimize the weights.The simulation results show that this method improves the training efficiency and classification accuracy for the intent recognition of hard-sample in imbalanced samples.
作者
赵亮
孙鹏
张杰勇
钟赟
杨富平
ZHAO Liang;SUN Peng;ZHANG Jieyong;ZHONG Yun;YANG Fuping(Information and Navigation School,Air Force Engineering University,Xi’an 710077,China;Unit 94587,Lianyungang 222345,Jiangsu,China)
出处
《空军工程大学学报》
CSCD
北大核心
2024年第1期76-82,共7页
Journal of Air Force Engineering University
基金
国家自然科学基金(61773396)。
关键词
意图识别
时空卷积网络
自注意力机制
难分类样本
样本不平衡
intent recognition
temporal convolution networks(TCN)
self-attention
hard-sample
imbalance sample
