RSSI-based Localization with Adaptive Noise Covariance Estimation for Resilient Multi-Agent Formations

TL;DR

This paper introduces an RSSI-based localization method with adaptive noise covariance estimation to enhance resilience of multi-agent systems against sensor faults and cyber attacks, ensuring reliable positioning in unknown environments.

Contribution

It proposes a multilateration scheme combined with adaptive Kalman filtering to improve localization robustness when agents' sensors are compromised or unreliable.

Findings

Effective in simulations with faults and cyber attacks

Improves localization accuracy under sensor compromise

Enhances resilience of multi-agent formations

Abstract

Typical cooperative multi-agent systems (MASs) exchange information to coordinate their motion in proximity-based control consensus schemes to complete a common objective. However, in the event of faults or cyber attacks to on-board positioning sensors of agents, global control performance may be compromised resulting in a hijacking of the entire MAS. For systems that operate in unknown or landmark-free environments (e.g., open terrain, sea, or air) and also beyond range/proximity sensing of nearby agents, compromised agents lose localization capabilities. To maintain resilience in these scenarios, we propose a method to recover compromised agents by utilizing Received Signal Strength Indication (RSSI) from nearby agents (i.e., mobile landmarks) to provide reliable position measurements for localization. To minimize estimation error: i) a multilateration scheme is proposed to leverage RSSI and position information received from neighboring agents as mobile landmarks and ii) a Kalman filtering method adaptively updates the unknown RSSI-based position measurement covariance matrix at runtime that is robust to unreliable state estimates. The proposed framework is demonstrated with simulations on MAS formations in the presence of faults and cyber attacks to on-board position sensors.