Multiple Target Tracking Using Random Finite Sets

Abstract

Multiple target tracking (MTT) plays a crucial role in guidance, navigation, and control of autonomous systems. However, it presents challenges in terms of computational complexity, measurement-to-track association ambiguity, clutter, and miss detection. The first half of the dissertation looks into multiple extended target tracking on a moving platform using cameras and a Light Detection and Ranging (LiDAR) scanner. A Bayesian framework is first designed for simultaneous localization and mapping and detection of dynamic objects. Two random finite sets filters are developed to track the extracted dynamic objects. First, the Occupancy Grid (OG) Gaussian Mixture (GM) Probability Hypothesis Density (PHD) filter jointly tracks the target kinematic states and a modified occupancy grid map representation of the target shape. The OG-GM-PHD filter successfully reconstructed the shape of the targets and resulted in a lower Optimal Sub-Pattern Assignment (OSPA) error metric than the traditional GM-PHD filter. The second MTT filter (Classifying Multiple Model (CMM) Labeled Multi Bernoulli (LMB)) is developed to leverage class-dependent motion characteristics. It fuses classification data from images to point cloud and incorporates object class probabilities into the tracked target states. This allows for better measurement-to-track associations and usage of class-dependent motion and birth models. The CMM-LMB filter is evaluated on KITTI dataset and simulated data from CARLA simulator. The CMM-LMB filter leads to a lower OSPA error metric than the Multiple Model LMB and LMB filters in both cases. The second half looks into sensor management for MTT using a sensor with a narrow field of view and a finite action slew rate. The sensor management for space situational awareness (SSA) is chosen as an application scenario. Classical sensor management algorithm for SSAtends to only consider the immediate reward. In this dissertation, deep reinforcement learning (DRL) agents are developed to overcome the combinatorial increase in problem size for long-term sensor tasking problems. A custom environment for SSA sensor tasking was developed in order to train and evaluate the DRL agents. The DRL agents are trained using Proximal Policy Optimization with Population Based Training and are able to outperform traditional myopic policies.