This paper addresses two challenges which are specific for tracking large amounts of vehicles within wide geographical areas. The first problem is related to the Earth’s curvature, which may degrade the prediction accuracy over long distances. The second problem is concerning the gating process, which may be slow. This paper proposes a single target tracking algorithm that predicts along the great circle, and thus minimizes the unmodelled prediction error, and a multi target gating algorithm that speeds up the gating process by using a combination of clustering and the n-vector position representation.

This paper considers a measurement-to-track association (MTA) problem of marine surface vessels in coastal regions. In particular, we consider a case where a set of non-cooperative measurements is associated with a set of existing tracks. We perform a case study involving marine vessels situated in a coastal environment dominated by islands, fjords and peninsulas. Euclidean distances, or more generally, Mahalanobis distances, between measurements and predicted positions may be an attractive approach due to its simplicity of implementation and low computational complexity. However, such metrics may not be feasible for association in coastal regions, as opposed to open waters. We propose an algorithm where numerical methods are used to produce a probability density map for evaluation of a set of non-cooperative measurements, obtained at a time between two positions belonging to a particular track. The proposed associator provides a list of hypotheses intended for a multiple hypothesis tracker (MHT), where each hypothesis is evaluated according to the deviation of expected arrival time.

The usual elliptical validation regions may not give a good representation of the possible vehicle positions if the time period of the prediction is long compared to the time scales of vehicle maneuvers. This paper proposes a fast, non-elliptical validation region giving a better coverage of the possible vehicle positions in such cases.