Enhanced Relative Localization Based on Persistent Excitation for Multi-UAVs in GPS-Denied Environments

Abstract

Intelligent unmanned aerial vehicles (UAVs) have been applied for civil and military uses. Relative localization (RL) is crucial for multi-UAVs to accomplish complex tasks successfully and safely. In global positioning system (GPS) denied environments, where accurate or meaningful location information is hard to obtain, persistent excitation based RL is a promising approach for multi-UAVs to achieve RL without any needs of external infrastructures. However, for many cases, existing persistent excitation based RL method suffers precision loss, error accumulation and divergence. This article tackles these issues, and proposes an enhanced approach to ensure the practical usage of persistent excitation based RL. Synchronized sensor sample prediction is introduced to confine and reduce RL error, and RL estimation is redesigned to avoid RL error accumulation. We evaluated our solution by simulating various scenarios. The results show that the proposed approach can effectively decrease RL error and prevent RL error accumulation.