Extended Object Tracking with Random Hypersurface Models

2013/01/01 by Marcus Baum, Baum, Marcus, Uwe D. Hanebeck +1 · 2 citations
Computer Science · Engineering · #FOS: Electrical engineering #Image Processing and 3D Reconstruction #Image and Object Detection Techniques #Robotics and Sensor-Based Localization #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1304.5084

openalex publication_date 2013/01/01 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28

Abstract

The Random Hypersurface Model (RHM) is introduced that allows for estimating a shape approximation of an extended object in addition to its kinematic state. An RHM represents the spatial extent by means of randomly scaled versions of the shape boundary. In doing so, the shape parameters and the measurements are related via a measurement equation that serves as the basis for a Gaussian state estimator. Specific estimators are derived for elliptic and star-convex shapes.

Citations

• Unscented Filtering and Nonlinear Estimation

Cited by

• Quadratic Kalman Filter for Elliptical Extended Object Tracking based on Decoupling State Components
• Multi-Sensor Fusion for Extended Object Tracking Exploiting Active and Passive Radio Signals

Related

• An extended target tracking model with multiple random matrices and unified kinematics
• Random Matrix Based Extended Target Tracking With Orientation: A New Model and Inference
• Tracking the Orientation and Axes Lengths of an Elliptical Extended Object
• Extended Object Tracking and Classification based on Linear Splines
• An Improved Random Matrix Prediction Model for Manoeuvring Extended Targets