In many applications of extended object tracking, 2-dimensional models are used due to the simplicity of the parametrization of the extent compared to a 3-dimensional model. However, many high-resolution sensors today provide a 3D point cloud which represents a challenge for how to relate these measurements with a 2D model. This is particularly relevant when modeling the extent using a parametrization of its contour, as with the random hypersurface model, since the measurements need to be related to the boundary of the object, whereas the measurements may also be generated from the interior of the object. The random hypersurface model uses a scaling factor to account for measurements being generated across the surface defined by the contour. In this article, we use the Gaussian process model and explore different ways to handle interior measurements either by using a boundary model, a surface model, or a combination of the two and we present a study on both simulated and real maritime data of these different approaches. The results show that using the boundary model results in a more precise extent estimate but one which could be underestimated, whereas incorporating the surface model results in a more conservative, overestimated extent. We also show that estimating the scaling factor for the surface model generally results in improved performance.

In maritime situational awareness, the Automatic Identification System (AIS) is a vital source of information. Recent work has explored the fusion of AIS information and exteroceptive measurements to improve maritime target tracking performance, also for extended object tracking. However, in extended object tracking, the discrepancy between the center of the ship and the position reported by the AIS system is no longer negligible and is a source of systemic bias, which can degrade tracking performance. In this paper, we introduce a method for estimating this discrepancy based on AIS information and the estimation provided by the Gaussian process target model from the exteroceptive sensor data. We use this method combined with an extended object Poisson multi-Bernoulli mixture (PMBM) filter to perform multiple extended object tracking. We also introduce a specific method for initialization of targets using AIS measurements in this filter. We validate the proposed method with LiDAR and AIS data, collected from an inland waterway in Belgium. The results show that compensating for the bias in this manner results in better tracking performance, primarily due to better initialization of new targets.

In Multiple Extended Object Tracking, the PMBM (Poisson Multi-Bernoulli Mixture) tracker is considered state-of-the-art. Originally, it was presented with the GGIW (Gamma Gaussian Inverse Wishart) target model, which is a random matrix model. When tracking larger objects using LiDAR, measurements are generated by the contour rather than the whole target surface, and it is beneficial to model this with the target model. A target model which has this capability is the Gaussian Process (GP) extent model. This paper presents a PMBM tracker using this target model. We also discuss considerations related to the use of the GP model in the PMBM framework. Secondly, we present improvements in the target model which increases the robustness of the model by dealing with the inherent nonlinearities using the Gauss-Newton method. We also present a comparison with the GGIW-PMBM tracker on simulated and real LiDAR data gathered from maritime vessels.