225 research outputs found
Maximum Correntropy Ensemble Kalman Filter
In this article, a robust ensemble Kalman filter (EnKF) called MC-EnKF is
proposed for nonlinear state-space model to deal with filtering problems with
non-Gaussian observation noises. Our MC-EnKF is derived based on maximum
correntropy criterion (MCC) with some technical approximations. Moreover, we
propose an effective adaptive strategy for kernel bandwidth selection.Besides,
the relations between the common EnKF and MC-EnKF are given, i.e., MC-EnKF will
converge to the common EnKF when the kernel bandwidth tends to infinity. This
justification provides a complementary understanding of the kernel bandwidth
selection for MC-EnKF. In experiments, non-Gaussian observation noises
significantly reduce the performance of the common EnKF for both linear and
nonlinear systems, whereas our proposed MC-EnKF with a suitable kernel
bandwidth maintains its good performance at only a marginal increase in
computing cost, demonstrating its robustness and efficiency to non-Gaussian
observation noises.Comment: Accepted by 62nd IEEE Conference on Decision and Control (CDC 2023
State Estimation of Wireless Sensor Networks in the Presence of Data Packet Drops and Non-Gaussian Noise
Distributed Kalman filter approaches based on the maximum correntropy
criterion have recently demonstrated superior state estimation performance to
that of conventional distributed Kalman filters for wireless sensor networks in
the presence of non-Gaussian impulsive noise. However, these algorithms
currently fail to take account of data packet drops. The present work addresses
this issue by proposing a distributed maximum correntropy Kalman filter that
accounts for data packet drops (i.e., the DMCKF-DPD algorithm). The
effectiveness and feasibility of the algorithm are verified by simulations
conducted in a wireless sensor network with intermittent observations due to
data packet drops under a non-Gaussian noise environment. Moreover, the
computational complexity of the DMCKF-DPD algorithm is demonstrated to be
moderate compared with that of a conventional distributed Kalman filter, and we
provide a sufficient condition to ensure the convergence of the proposed
algorithm
Generalized Multi-kernel Maximum Correntropy Kalman Filter for Disturbance Estimation
Disturbance observers have been attracting continuing research efforts and
are widely used in many applications. Among them, the Kalman filter-based
disturbance observer is an attractive one since it estimates both the state and
the disturbance simultaneously, and is optimal for a linear system with
Gaussian noises. Unfortunately, The noise in the disturbance channel typically
exhibits a heavy-tailed distribution because the nominal disturbance dynamics
usually do not align with the practical ones. To handle this issue, we propose
a generalized multi-kernel maximum correntropy Kalman filter for disturbance
estimation, which is less conservative by adopting different kernel bandwidths
for different channels and exhibits excellent performance both with and without
external disturbance. The convergence of the fixed point iteration and the
complexity of the proposed algorithm are given. Simulations on a robotic
manipulator reveal that the proposed algorithm is very efficient in disturbance
estimation with moderate algorithm complexity.Comment: in IEEE Transactions on Automatic Control (2023
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