  Path-tracking control of wheeled mobile robot (WMR) has gained a lot of
research attention, primarily because of its wide applicability -- for example
intelligent wheelchairs, exploration-assistant remote WMR. Recent increase in
remote and autonomous operations\requirements for WMR has led to more and more
use of IoT devices within the control loop. Consequently, providing interfaces
for malicious interactions through false data injection attacks (FDIA).
Moreover, optimization-based FDIAs have been shown to cause catastrophic
consequences in feedback control systems while by-passing any residual-based
monitoring system. Since these attacks target system measurement process, this
paper focuses on the problem of improving the resiliency of dynamical observers
against FDIA. Specifically, we propose an attack-resilient pruning algorithm
which attempts to exclude compromised channels from being processed by the
observer. The proposed pruning algorithm improves attack-localization precision
to $100\%$ with high probability, which correspondingly improves the resiliency
of the underlying UKF to FDIA. The improvements due to the developed resilient
pruning-based observer is validated through a numerical simulation of a
two-layer path-tracking control platform of differential-driven wheeled mobile
robot (DDWMR) under FDIA.
