This paper considers the fusion of multiple estimates of a spatially extended object, where the object extent is modeled as an ellipse that is parameterized by the orientation and semi-axes lengths. For this purpose, we propose a novel systematic approach that employs a distance measure for ellipses, i.e., the Gaussian Wasserstein distance, as a cost function. We derive an explicit expression for the Minimium Mean Gaussian Wasserstein distance (MMGW) estimate. Based on the concept of a MMGW estimator, we develop efficient methods for the fusion of extended target estimates. The proposed fusion methods are evaluated in a simulated experiment and the benefits of the novel methods are discussed.
This paper presents an approach to enable general tracking of extended objects of multiple sensors. Expert information in each input providing sensor module is mapped into simple model parameters and allows the fusion center to use a generalized version of such information. The model type and parameters are presented and a classical Kalman based fusion is extended with a method for integrated extent handling. By this approach the central fusion node can take into account both the track level information and extent estimate from each sensor. The proposed method is compared with a classic method of fusing the object center and the extent estimate separately. Simulated data is used to show that our proposed approach is general in the sense that it can be used on various setups without adaption. Detailed performance results are given based on estimation errors of the extended object space vector. The findings based on simulated data are completed by real world data from a front facing sensor setup. It is shown that the proposed method offers a benefit in position accuracy, especially when the measurement information does not contain a complete extent information in all directions.