Object Tracking with Sensor Fusion - An Interactive Learning Tool

Body tracking plays a key role in autonomous navigation applications. Behavior that resists inertia can be modelled as a dynamical system, wherein the kinematic component is constituted by the action of motion. Such a system may then be subjected to estimation algorithms and control laws formulated by systems theory, according to the specific problem domain for which it is modelled. This paper presents a detailed comparison of three main statistical algorithms for estimating dynamical system parameters: the linear, extended, and unscented Kalman filters. The body motion is intercepted by sensor fusion. To facilitate visual validation and concretization of the theoretical notions presented, a two-dimensional (2D) game-like graphical application has been developed to enhance user comprehension.