Tracking the Orientation and Axes Lengths of an Elliptical Extended Object
For tracking applications requiring explicit ellipse parameter estimation, this provides a computationally efficient alternative to Monte Carlo methods.
The paper presents a recursive Kalman filter with closed-form expressions for tracking the orientation and axes lengths of an elliptical extended object, outperforming existing Monte Carlo or incomplete parameter approaches in simulations.
Extended object tracking considers the simultaneous estimation of the kinematic state and the shape parameters of a moving object based on a varying number of noisy detections. A main challenge in extended object tracking is the nonlinearity and high-dimensionality of the estimation problem. This work presents compact closed-form expressions for a recursive Kalman filter that explicitly estimates the orientation and axes lengths of an extended object based on detections that are scattered over the object surface (according to a Gaussian distribution). Existing approaches are either based on Monte Carlo approximations or do not allow for explicitly maintaining all ellipse parameters. The performance of the novel approach is demonstrated with respect to the state-of-the-art by means of simulations.