Extended Object Tracking with Random Hypersurface Models 论文
2014IEEE Transactions on Aerospace and Electronic Systems引用 274
Image Processing and 3D ReconstructionImage and Object Detection TechniquesRobotics and Sensor-Based Localization
摘要
The random hypersurface model (RHM) is introduced 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.