Trustworthy UAV Cooperative Localization: Information Analysis of Performance and Security

TL;DR

This paper develops a trustworthy cooperative localization framework for UAV systems, deriving bounds, addressing mobility, and enhancing security against falsified data with anomaly detection and reputation mechanisms.

Contribution

It introduces a comprehensive framework combining CRLB analysis, mobility adaptation, and security strategies including TAD and RP for UAV cooperative localization.

Findings

CRLB derived for 3D cooperative localization with non-uniform anchors

Proposed TAD effectively detects falsified information under resource constraints

Numerical simulations validate the robustness and effectiveness of the proposed methods

Abstract

This paper presents a trustworthy framework for achieving accurate cooperative localization in multiple unmanned aerial vehicle (UAV) systems. The The Cramer-Rao Lower Bound (CRLB) for the three-dimensional (3D) cooperative localization network is derived, with particular attention given to practical scenarios involving non-uniform spatial distribution of anchor nodes. Challenges of mobility are then addressed with Mobility Adaptive Gradient Descent (MAGD). In the context of system security, we derive the CRLB of localization under the influence of falsified information. The methods and strategies of injecting such information and their impact on system performance are studied. To assure robust performance under falsified data, we propose a mitigation solution named Time-evolving Anomaly Detection (TAD). Furthermore, we model the system performance regarding the density and magnitude of falsified information, focusing on realistic scenarios where the adversary is resource-constrained. With the vulnerability of cooperative localization understood, we apply TAD and formulate an optimization problem from the adversary's perspective. Next, we discuss the design principles of an anomaly detector, with emphasis of the trade-off of reducing such optimum and system performance. Additionally, we also deploy a reputation propagation (RP) mechanism to fully utilize the anomaly detection and further optimize the TAD. Our proposed approaches are demonstrated through numerical simulations.