Extended Target Tracking of Convex Polytope Shapes with Maneuvers and Clutter

Abstract

High resolution sensors such as automotive radar and LiDAR have become prevalent in target tracking applications in recent times. Data from such sensors demands extended target tracking in which, the shape of the target is to be estimated along with the kinematics. Several applications benefit from extended target tracking, for example, autonomous vehicles and robotics. This thesis proposes a different approach to extended target tracking compared to existing literature. Instead of a single shape descriptor to describe the entire target shape, different parts of the extended target are assumed to be distinct targets constrained by the target rigid body shape. This formulation is able to handle issues such as self-occlusion and clutter which, are not addressed sufficiently in literature. Firstly, a framework for extended target tracking is developed based on the formulation proposed. Using 2D convex hull as a shape descriptor, an algorithm to track 2D convex polytope shaped targets is developed. Further, the point target Probabilistic Multiple Hypotheses Tracker (PMHT) is modified to derive an extended target PMHT (ET-PMHT) equations to track 3D convex polytope shapes, using a Delaunay triangulation to describe the shape. Finally, the approach is extended to handle target maneuvers, as well as, clutter and measurements from the interior of the target. In all three cases, the issue of self-occlusion is considered and the algorithms are still able to effectively capture the target shape. Since the true target center may not be observable, the shape descriptor abandons the use of target center in the state, and the shape is described by its boundary alone. The shape descriptors also support addition and deletion of faces, which is useful for handling newly visible parts of the target and clutter, respectively. The algorithms proposed have been compared with the existing literature for various scenarios, and it is seen that the proposed algorithms outperform, especially in the presence of self-occlusion.