Enhancing Dynamic Target Estimation and UAV Swarm Observability-Based Coverage Control

This study introduces a coverage control algorithm designed to optimize the monitoring and tracking of targets utilizing camera-equipped drones. The algorithm tackles the challenge posed by the limited sensing range of cameras by harnessing the flexibility of drones to achieve comprehensive target coverage effectively. In addition to enhancing coverage, the algorithm places emphasis on augmenting estimation accuracy to bolster system stability and facilitate future applications. It specifically targets estimation errors that are typically overlooked in conventional coverage control methods, with the aim of refining accuracy. A crucial criterion in this endeavor is the determination of the state covariance lower limit, which can be assessed through observability evaluation. In consideration of the probability of target occurrence, the primary objective is to ensure continuous coverage of areas with high information density within the field of view of drone-equipped cameras. Observability plays a pivotal role in promoting efficient collaboration through the exchange of information and positional data. To minimize errors, factors such as target locations, estimation accuracy, and camera characteristics on each drone are taken into account. In scenarios involving communication between drones, the exchange of observational data enables the estimation of target groups. The algorithm leverages observability as a decision-making indicator along with a Control Barrier Function to ensure that drones steer clear of losing track of targets. The effectiveness of the developed controller is corroborated through simulations.