Tracking the Orientation and Axes Lengths of an Elliptical Extended Object

Abstract

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.

This is the accepted version (not the IEEEpublished version). \c{opyright} 20XX IEEE

References in corpus (1)

• Extended Object Tracking: Introduction, Overview and Applications

Cited by in corpus (9)

• Random Matrix Based Extended Target Tracking with Orientation: A New Model and Inference
• Scalable Detection and Tracking of Geometric Extended Objects
• Distributed Extended Object Tracking Using Coupled Velocity Model from WLS Perspective
• A Batch Update Using Multiplicative Noise Modelling for Extended Object Tracking
• Poisson Multi-Bernoulli Approximations for Multiple Extended Object Filtering
• Optimal Fusion of Elliptic Extended Target Estimates based on the Wasserstein Distance
• An Improved Random Matrix Prediction Model for Manoeuvring Extended Targets
• A Rao-Blackwellized Particle Filter for Superelliptical Extended Target Tracking
• Distributed Extended Object Tracking Information Filter Over Sensor Networks