Finite Set Statistics Based Multi-object Tracking: Recent Advances, Challenges, and Space Applications

Keith LeGrand,  Sandia National Lab


Abstract: Multi-object tracking is the process of simultaneously estimating an unknown number of objects and their partially hidden states using unlabeled noisy measurement data. Common applications of multi-object tracking algorithms include space situational awareness (SSA), missile defense, pedestrian tracking, and airborne surveillance. In recent years, a new branch of statistical calculus known as finite set statistics (FISST) has provided a formalism for solving such tracking problems and has resulted in a renaissance in tracking research. Today, researchers are applying FISST to formalize and solve problems not typically thought of as traditional tracking problems, such as robotic simultaneous localization and mapping (SLAM), obstacle localization for driverless vehicles, lunar descent and landing, and autonomous swarm control. This talk discusses the basic principles of multi-object tracking with a focus on FISST and highlights recent advancements. Special challenges, such as probabilistic object appearance detection, extended object tracking, and distributed multi-sensor fusion are presented. Finally, this talk will present the latest application of FISST theory to sensor planning, whereby multi-object information measures are used to optimize the performance of large dynamic sensor networks.