基于元学习与循环神经网络的战场态势实时智能感知系统
A real-time battle situation intelligent awareness system based on Meta-learning & RNN
- 采用元学习处理战场数据，实现多步清洗融合与持续更新
- 分步递进建模捕捉数据时序依赖，预测敌方行动路径
- 适用于战时指挥决策支持，提升战场态势理解能力
现代战争中，实时准确的战场态势分析对战略战术决策至关重要。本文提出的实时战场态势智能感知系统（BSIAS）结合元学习分析与分步RNN建模：前者完成战场数据的多步处理与持续更新，包括数据清洗、融合、挖掘；后者通过逐步捕捉数据集的时间依赖关系，优化战场建模。以模拟作战为例，该系统可预测各方可能的移动方向与进攻路线，为指挥员提供智能化决策支持。本研究展示了集成式BSIAS在战场指挥与分析工程中的应用潜力。
原文摘要 · Abstract (English)
In modern warfare, real-time and accurate battle situation analysis is crucial for making strategic and tactical decisions. The proposed real-time battle situation intelligent awareness system (BSIAS) aims at meta-learning analysis and stepwise RNN (recurrent neural network) modeling, where the former carries out the basic processing and analysis of battlefield data, which includes multi-steps such as data cleansing, data fusion, data mining and continuously updates, and the latter optimizes the battlefield modeling by stepwise capturing the temporal dependencies of data set. BSIAS can predict the possible movement from any side of the fence and attack routes by taking a simulated battle as an example, which can be an intelligent support platform for commanders to make scientific decisions during wartime. This work delivers the potential application of integrated BSIAS in the field of battlefield command & analysis engineering.
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