  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.
