Sensing what matters

Recently, the Royal Netherlands Navy (RNLN) is executing missions in coastal regions with a lot of civil traffic. Furthermore, the opponent of a typical modern mission is not as apparent as was the case during e.g., the Cold War. In the direct vicinity of naval vessels are many objects and it is increasingly complex to identify which of those objects pose a threat. This is the main reason for the need of decision support and automation aboard RNLN’s ships. This thesis introduces a new classification methodology that is suited for the military application domain. This methodology is based on fitting the incoming sensor information on predefined situation knowledge inserted by the operator. To verify the performance new evaluation criteria are introduced that are suited for the characteristics of the application domain. Multiple classifiers result from this new methodology and their results are combined using Dezert-Smarandache Theory. The performance gain of this new approach is shown in a simulation and using existing and new evaluation criteria compared to other known classifiers. The system introduced in this thesis additional has advantages in terms of user interaction. Furthermore, this new system enables the automation of describing the information requirements for classification. This in turn enables the automation of sensor management processes. Finally, this thesis argues that it is essential to integrate existing sensor performance programs in order to automate sensor management.