In autonomous vehicle systems, it is paramount to detect other objects in the vicinity and track their movement. Extended Object Tracking (EOT) provides a convenient framework for tracking objects using high-resolution sensor data by defining models for the object’s spatial dimensions (a.k.a. extent). In maritime applications, the objects of interest are mainly other maritime vessels, and these vary greatly in shape and size. This diversity proves to be a challenge for defining general extent models that both give accurate representations for most vessels and that do not depend on a large number of parameters. In this paper, a general three-dimensional low-parameter ship hull model designed for EOT is presented. The presented extent model is constructed by intertwining a polynomial representation along the vertical direction with a frequency representation along the horizontal plane. However, to reduce the dimension of the parameter space without compromising its accuracy, the horizontal frequency representation is modified by performing a Principal Component Analysis (PCA). In particular, this extent representation does not require an underlying discretization grid, which makes the model scalable and therefore well-suited for modeling objects that vary greatly in size.

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.

Extended Object Tracking (EOT) is a advantageous technique for achieving situational awareness in autonomous vehicle systems. The EOT problem is to both estimate the movement and spatial dimensions of an object using high-resolution measurements. In the case of laser measurements or other types of measurements that correspond to points on the object’s boundary, the true measurement model of the EOT problem is based on an implicit equation for the measurement coordinates. This intrinsic implicity is often not addressed directly in several EOT models found in the literature. In this paper, the EOT problem is reformulated as a least square minimization problem without compromising the original implicit measurement model by introducing an extra variable for each measurement. In addition, this new least squares formulation allows considering measurements and state variables for a whole time window, and not just a single time step. An EOT algorithm based on solving the derived least squares minimization problem is proposed and tested with simulated scenarios.