Cooperative Processing and Learning Methods for High-Resolution Environmental Perception

Abstract

AbstractCooperative positioning approaches enable interconnected agents to share information across the network, thereby improving accuracy, reliability, and safety compared to conventional single-agent localization methods. This chapter presents novel cooperative localization and learning strategies to provide precise positioning in harsh propagating environments as well as reliable environmental mapping for highly-dynamic scenarios. At first, positioning and environmental perception tasks are addressed separately. More specifically, augmentation strategies are proposed to improve positioning accuracy in complex environments by exploiting prior information on the tracking environment. Next, decentralized Federated Learning (FL) policies are developed to obtain accurate environmental sensing at the agents in a privacy-preserving and communication-efficient manner. Then, the localization and environmental perception problems are solved via a unified solution by designing a data-driven cooperative strategy where agents collaborate to enhance their environmental awareness and their positioning capabilities concurrently. Finally, Bayesian FL tools are developed so that the agents are able to incorporate uncertainty in their decisions and consequently provide trustworthy environmental perception. The achieved results show how the proposed techniques can enable accurate, communication-efficient, and trustworthy localization and sensing.