AI 中文总结
针对空中目标分类和意图预测问题，提出基于短序列子样本预测、经证据推理框架管理不确定性的集成方法，生成用于评估的数据集，案例研究表明该方法在目标类型分类和意图预测上分别有88%和93%的准确率。
AI 中文摘要
及时对空中目标进行分类和意图预测对于战斗机做出明智战术决策至关重要。当前空中目标分类方法依赖使用时间序列数据的数据驱动模型，虽对长时间数据表现良好，但在高风险且需快速决策的战斗场景中不实用。本文提出一种集成方法，能在威胁需快速响应的场景下基于部分数据决策。该方法从短序列子样本生成预测，通过跨子样本传播信念细化结果，经证据推理框架组合分类器输出管理不确定性，用基于规则技术和基于距离的组合方法推断目标意图。因缺乏公开数据集，生成了用于评估的空中目标分类数据集。通过涉及八个目标的案例研究证明了该方法的有效性，目标类型分类和意图预测准确率分别达88%和93%。
英文摘要
Timely classification and intent prediction of aerial targets is crucial for a combat aircraft to make informed tactical decisions. The prevailing approach for aerial target classification relies on data-driven models using time-series data. These models perform well with long-duration data; however, this is impractical in combat situations involving rapidly evolving threats that demand quick decisions. Minimizing false predictions is essential, as uncertainty is preferable to incorrect assessments in high-risk environments. Here, we propose an integrated approach to target classification and intent prediction that enables decisions from partial data in settings where threats require rapid response. In the proposed method, predictions are generated from short sequential sub-samples instead of the entire time series, and the results are refined by propagating beliefs across sub-samples. Outputs from classifiers are combined through an evidential reasoning framework to manage uncertainty. Target intent is inferred using rule-based techniques and a distance-based combination method to fuse information over time. Due to lack of publicly available datasets, a dataset for aerial target classification was generated for evaluation. A case study involving eight targets is used to demonstrate the effectiveness of the approach, whereby accuracies of 88% and 93% are achieved for target type classification and intent prediction, respectively.
