  Cyber-resilience is an increasing concern in developing autonomous navigation
solutions for 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, a first stage extracts shoreline features from radar and matches them
to the electronic navigation chart. A 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, that is agnostic to the underlying technology of
the individual sensory input. A combined parametric Gaussian modelling and
Kernel Density Estimation is suggested and compared with a generalized
likelihood ratio change detector that uses sliding windows. The paper shows how
deviations from nominal behaviour and isolation of the components is possible
when under attack or when defects in sensors occur.
