Dissecting uncertainty-based fusion techniques for maritime anomaly detection

Detecting and classifying anomalies for Maritime Situation Awareness gets a lot of benefit from the combination of multiple sources, correlating their output for detecting inconsistencies in vessels' behaviour. Adequate uncertainty representation and processing is crucial for this higher-level task where the operator analyses information correlating with his background knowledge. This paper addresses the problem of performance criteria selection and definition for information fusion systems in their ability to handle uncertainty. Indeed, i addition to the classical algorithmic performances of accuracy or timeliness, other aspects such as the interpretation, simplicity, expressiveness need to be considered in the design of the technique for uncertainty management for a improved synergy between the human and the system. In this paper, we dissect several uncertainty representation and reasoning techniques (URRTs) addressing a fusion problem for maritime anomaly detection. The uncertainty supports are identified as a basis for the global expressiveness criterion. A selection of six elementary URRTs are described and compared according to their expressiveness power of uncertainty, using the Uncertainty Representation and Reasoning Framework (URREF) ontology. This study is considered as preliminary to guide further development and implementation of fusion algorithms for maritime anomaly detection, and the definition of associated criteria and measures of performance.