A Reliable and Resilient Framework for Multi-UAV Mutual Localization

TL;DR

This paper introduces a robust framework for mutual localization in multi-UAV systems, combining adaptive algorithms and anomaly detection to improve accuracy and security in dynamic and potentially adversarial environments.

Contribution

The paper proposes a novel framework integrating MAGD and TAD with reputation schemes to enhance localization accuracy and security in multi-UAV systems.

Findings

The framework achieves accurate localization in dynamic UAV configurations.

The TAD effectively detects and mitigates malicious data attacks.

Numerical simulations validate the robustness and security of the proposed methods.

Abstract

This paper presents a robust and secure framework for achieving accurate and reliable mutual localization in multiple unmanned aerial vehicle (UAV) systems. Challenges of accurate localization and security threats are addressed and corresponding solutions are brought forth and accessed in our paper with numerical simulations. The proposed solution incorporates two key components: the Mobility Adaptive Gradient Descent (MAGD) and Time-evolving Anomaly Detectio (TAD). The MAGD adapts the gradient descent algorithm to handle the configuration changes in the mutual localization system, ensuring accurate localization in dynamic scenarios. The TAD cooperates with reputation propagation (RP) scheme to detect and mitigate potential attacks by identifying UAVs with malicious data, enhancing the security and resilience of the mutual localization