Optimal Fusion of Elliptic Extended Target Estimates based on the Wasserstein Distance
This work addresses the fusion of extended target estimates for applications like tracking or sensor networks, but it appears incremental as it builds on existing distance measures and estimation concepts.
The paper tackles the problem of fusing multiple estimates of an elliptical extended target by proposing a novel approach using the Gaussian Wasserstein distance as a cost function, deriving an explicit approximate expression for the Minimum Mean Gaussian Wasserstein distance estimate, and developing efficient fusion methods evaluated in simulations.
This paper considers the fusion of multiple estimates of a spatially extended object, where the object extent is modeled as an ellipse parameterized by the orientation and semiaxes 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 approximate expression for the Minimum 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.