Traditional Kalman filter (KF) is derived under the well-known minimum mean square error
(MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals …

Secure state estimation is the problem of estimating the state of a dynamical system from a
set of noisy and adversarially corrupted measurements. Intrinsically a combinatorial …

We consider the problem of estimating the state of a noisy linear dynamical system when an
unknown subset of sensors is arbitrarily corrupted by an adversary. We propose a secure …

We introduce a scalable observer architecture, which can efficiently estimate the states of a
discrete-time linear-time-invariant system whose sensors are manipulated by an attacker …

State-space smoothing has found many applications in science and engineering. Under
linear and Gaussian assumptions, smoothed estimates can be obtained using efficient …

A robust fixed-lag smoothing approach is proposed in the case there is a mismatch between
the nominal model and the actual model. The resulting robust smoother is characterized by …

We focus on securely estimating the state of a nonlinear dynamical system from a set of
corrupted measurements for two classes of nonlinear systems, and propose a technique that …

Despite the higher estimation accuracy of the maximum correntropy Kalman filter (MCKF)
algorithm in system with non-Gaussian noise, its estimation performance will decrease when …

This paper details how to parameterize the posterior distribution of state-space systems to
generate improved optimization problems for system identification using variational …

This paper introduces the time synchronization attack rejection and mitigation (TSARM)
technique for time synchronization attacks over the global positioning system (GPS). The …