🤖 AI Summary
This work addresses the challenges of classifying CBRNE threats using heterogeneous sensors, which suffer from limited sensing capabilities, significant reliability disparities, abundant indirect indicators, and high levels of clutter, compounded by the scarcity of high-quality labeled data. To overcome these issues, the authors propose an evidence hierarchy that integrates direct observations, indirect indicators, and contextual information. By incorporating open-source intelligence (OSINT) to enrich environmental context and leveraging domain knowledge to construct Bayesian priors, the framework enables robust fusion of multi-source heterogeneous sensor data for threat classification. Experimental results demonstrate that the proposed approach achieves 95% overall classification accuracy in simulated scenarios, significantly enhancing system robustness against clutter interference and prior mismatch.
📝 Abstract
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%.