2019/07/23 by Shishan Yang, Marcus Baum · 1 citation
Computer Science · Engineering · #Advanced Measurement and Detection Methods #Robotics and Sensor-Based Localization #Target Tracking and Data Fusion in Sensor Networks
paper · doi:10.1109/tsp.2019.2929462
openalex publication_date 2019/07/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
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. The main challenge in extended object tracking is the nonlinearity and high dimensionality of the estimation problem. This study 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.