Cyber-resilience for marine navigation by information fusion and change detection

Cyber-resilience is an increasing concern for autonomous navigation of marine vessels. This paper scrutinizes cyber-resilience properties of marine navigation through a prism with three edges: multiple sensor information fusion, diagnosis of not-normal behaviours, and change detection. It proposes a two-stage estimator for diagnosis and mitigation of sensor signals used for coastal navigation. Developing a Likelihood Field approach, the first stage extracts shoreline features from radar and matches them to the electronic navigation chart. The second stage associates buoy and beacon features from the radar with chart information. Using real data logged at sea tests combined with simulated spoofing, the paper verifies the ability to timely diagnose and isolate an attempt to compromise position measurements. A new approach is suggested for high level processing of received data to evaluate their consistency, which is agnostic to the underlying technology of the individual sensory input. A combined generalized likelihood ratio test using both parametric Gaussian modelling and Kernel Density Estimation is suggested and compared with a detector using only either of two. The paper shows how the detection of deviations from nominal behaviour is possible when the navigation sensor is under attack or defects occur.