Multi-Hypotheses Navigation in Collaborative Localization subject to Cyber Attacks

TL;DR

This paper proposes a resilient multi-hypotheses approach for collaborative localization in multi-agent systems under cyber attacks, effectively distinguishing truthful data from spoofed measurements to improve navigation accuracy.

Contribution

It introduces a novel multi-hypotheses framework with geometric hypothesis reduction techniques to enhance robustness against spoofing in multi-agent localization.

Findings

The approach effectively isolates spoofed measurements from truthful data.

Hypotheses reduction controls the spread of false information across the network.

Conservative fusion impacts detection speed and accuracy.

Abstract

This paper addresses resilient collaborative localization in multi-agent systems exposed to spoofed radio frequency measurements. Each agent maintains multiple hypotheses of its own state and exchanges selected information with neighbors using covariance intersection. Geometric reductions based on distance tests and convex hull structure limit the number of hypotheses transmitted, controlling the spread of hypotheses through the network. The method enables agents to separate spoofed and truthful measurements and to recover consistent estimates once the correct hypothesis is identified. Numerical results demonstrate the ability of the approach to contain the effect of adversarial measurements, while also highlighting the impact of conservative fusion on detection speed. The framework provides a foundation for resilient multi-agent navigation and can be extended with coordinated hypothesis selection across the network.