An Evidence Hierarchy for Bayesian Object Classification via OSINT-Aided Heterogeneous Sensor Fusion

TL;DR

This paper introduces a Bayesian threat classification framework that integrates heterogeneous sensor data and open-source intelligence to improve detection accuracy and robustness in CBRNE threat scenarios.

Contribution

It proposes a novel evidence hierarchy and a domain knowledge-enhanced fusion process incorporating OSINT for improved threat classification.

Findings

Achieved up to 95% classification accuracy in simulated scenarios.

Demonstrated robustness to clutter and prior mismatch.

Validated the effectiveness of the evidence hierarchy and fusion approach.

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%.