A Surveillance Application of Satellite AIS Utilizing a Parametric Model for Probability of Detection

The question of having sufficient surveillance capability to detect illicit behaviour in order to inform decision makers in a timely fashion is of the ultimate importance to defence, security, law enforcement, and regulatory agencies. Quantifying such capability provides a means of informing asset allocation, as well as establishing the link to risk of mission failure. Individual sensor models can be built and integrated into a larger model that layers sensor performance using a set of metrics that can take into account area coverage, coverage times, revisit rates, detection probabilities, and error rates. This paper describes an implementation of a parametric model for Satellite Automated Identification System (S-AIS) sensor performance. Utilizing data from a real data feed, the model was able to determine the percentage of uncorrupted S-AIS messages and the probability of detection of at least one correct S-AIS message received during an observation interval. It is important to note that the model implementation was not actively calculating the effect of message overlap based on satellite altitude and footprint width, or reductions in collisions due to signal de-collision algorithms.