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.

A multi-target tracking method requires a data association algorithm to decide which measurements, from a set of many, to use for updating each track. In this paper we will consider a case where the position of multiple marine vessels is measured in a coastal region, and the time span between each measurement set is large. The vessels of interest are assumed to be sufficiently inert in the sense that the vessels intend to maintain a particular cruising speed while traveling to their destination. Moreover, by assuming that the vessels intend to travel the shortest route possible, we have a foundation to assign conditional probabilities to each measured position. We use the fast marching method (FMM) to compute a time-of-arrival (ToA) map which indicates where the vessel can be at a particular time, provided that the initial speed is known and remains constant. The ToA map is then evaluated with respect to a Gaussian distribution which provides a probability density function (pdf) used to evaluate the probability that a new measurement originate from the same vessel as a track.