用开源情报提升多传感器融合的威胁分类准确率
An Evidence Hierarchy for Bayesian Object Classification via OSINT-Aided Heterogeneous Sensor Fusion
- 构建证据层级，融合直接、间接和环境信息
- 引入开源情报增强上下文，使分类准确率达95%
- 适合智能安防与反恐检测场景使用
异构传感器融合对检测、定位和分类化学、生物、放射、核及爆炸（CBRNE）威胁至关重要。然而，单一传感器往往只能探测部分威胁，且可靠性不一，甚至仅提供间接线索，导致威胁分类困难。此外，传感器端高杂波率给融合系统带来巨大挑战。同时，高质量数据集稀缺制约了智能传感器中学习型检测与分类模型的发展。为此，提出一种融入领域知识的上下文感知融合方法：首先建立新型证据层级，用于建模直接、指示性及情境信息；其次通过采集、处理和利用开源情报（OSINT）输入，将环境上下文引入融合过程；最后，基于证据层级各层信息，结合领域知识先验，构建贝叶斯威胁类型分类机制。在模拟场景中评估表明，该方法在抗杂波和先验不匹配方面表现更稳健，整体分类准确率最高达95%。
原文摘要 · Abstract (English)
Heterogeneous sensor fusion is vital for detecting, localizing, and classifying CBRNE threats. However, individual sensors are often only capable of detecting a subset of relevant threats with varying reliability or can even provide only indirect threat indications, making threat classification challenging. Furthermore, high clutter rates on the sensor side present a great challenge for fusion systems. Additionally, the limited availability of high quality datasets hinders the advancement of learning-based detection and classification models in smart sensors. To mitigate these sensor related shortcomings, a context-aware and domain knowledge-enhanced fusion process is proposed. First, a novel evidence hierarchy is established that enables modeling of direct, indicative, and contextual information. Second, contextual information about the environment is introduced into the fusion process, by collecting, processing, and exploiting OSINT inputs. Third, all levels of the evidence hierarchy are used to craft a Bayesian threat type classification mechanism with domain knowledge-informed priors. The proposed methodology is evaluated in simulated scenarios, and the results demonstrate the benefit of the proposed fusion approach in terms of robustness to clutter and prior mismatch, with an overall classification accuracy of up to 95%.
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