327 research outputs found
Maximum Correntropy Derivative-Free Robust Kalman Filter and Smoother
We consider the problem of robust estimation involving filtering and
smoothing for nonlinear state space models which are disturbed by heavy-tailed
impulsive noises. To deal with heavy-tailed noises and improve the robustness
of the traditional nonlinear Gaussian Kalman filter and smoother, we propose in
this work a general framework of robust filtering and smoothing, which adopts a
new maximum correntropy criterion to replace the minimum mean square error for
state estimation. To facilitate understanding, we present our robust framework
in conjunction with the cubature Kalman filter and smoother. A half-quadratic
optimization method is utilized to solve the formulated robust estimation
problems, which leads to a new maximum correntropy derivative-free robust
Kalman filter and smoother. Simulation results show that the proposed methods
achieve a substantial performance improvement over the conventional and
existing robust ones with slight computational time increase
Minimum Error Entropy Kalman Filter
To date most linear and nonlinear Kalman filters (KFs) have been developed
under the Gaussian assumption and the well-known minimum mean square error
(MMSE) criterion. In order to improve the robustness with respect to impulsive
(or heavy-tailed) non-Gaussian noises, the maximum correntropy criterion (MCC)
has recently been used to replace the MMSE criterion in developing several
robust Kalman-type filters. To deal with more complicated non-Gaussian noises
such as noises from multimodal distributions, in the present paper we develop a
new Kalman-type filter, called minimum error entropy Kalman filter (MEE-KF), by
using the minimum error entropy (MEE) criterion instead of the MMSE or MCC.
Similar to the MCC based KFs, the proposed filter is also an online algorithm
with recursive process, in which the propagation equations are used to give
prior estimates of the state and covariance matrix, and a fixed-point algorithm
is used to update the posterior estimates. In addition, the minimum error
entropy extended Kalman filter (MEE-EKF) is also developed for performance
improvement in the nonlinear situations. The high accuracy and strong
robustness of MEE-KF and MEE-EKF are confirmed by experimental results.Comment: 12 pages, 4 figure
Robust Student's t based Stochastic Cubature Filter for Nonlinear Systems with Heavy-tailed Process and Measurement Noises
In this paper, a new robust Student's t based stochastic cubature filter
(RSTSCF) is proposed for nonlinear state-space model with heavy-tailed process
and measurement noises. The heart of the RSTSCF is a stochastic Student's t
spherical radial cubature rule (SSTSRCR), which is derived based on the
third-degree unbiased spherical rule and the proposed third-degree unbiased
radial rule. The existing stochastic integration rule is a special case of the
proposed SSTSRCR when the degrees of freedom parameter tends to infinity. The
proposed filter is applied to a manoeuvring bearings-only tracking example,
where an agile target is tracked and the bearing is observed in clutter.
Simulation results show that the proposed RSTSCF can achieve higher estimation
accuracy than the existing Gaussian approximate filter, Gaussian sum filter,
Huber-based nonlinear Kalman filter, maximum correntropy criterion based Kalman
filter, and robust Student's t based nonlinear filters, and is computationally
much more efficient than the existing particle filter.Comment: 9 pages, 2 figure
Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments
Sparse adaptive channel estimation problem is one of the most important
topics in broadband wireless communications systems due to its simplicity and
robustness. So far many sparsity-aware channel estimation algorithms have been
developed based on the well-known minimum mean square error (MMSE) criterion,
such as the zero-attracting least mean square (ZALMS), which are robust under
Gaussian assumption. In non-Gaussian environments, however, these methods are
often no longer robust especially when systems are disturbed by random
impulsive noises. To address this problem, we propose in this work a robust
sparse adaptive filtering algorithm using correntropy induced metric (CIM)
penalized maximum correntropy criterion (MCC) rather than conventional MMSE
criterion for robust channel estimation. Specifically, MCC is utilized to
mitigate the impulsive noise while CIM is adopted to exploit the channel
sparsity efficiently. Both theoretical analysis and computer simulations are
provided to corroborate the proposed methods.Comment: 29 pages, 12 figures, accepted by Journal of the Franklin Institut
Multi-Kernel Correntropy for Robust Learning
As a novel similarity measure that is defined as the expectation of a kernel
function between two random variables, correntropy has been successfully
applied in robust machine learning and signal processing to combat large
outliers. The kernel function in correntropy is usually a zero-mean Gaussian
kernel. In a recent work, the concept of mixture correntropy (MC) was proposed
to improve the learning performance, where the kernel function is a mixture
Gaussian kernel, namely a linear combination of several zero-mean Gaussian
kernels with different widths. In both correntropy and mixture correntropy, the
center of the kernel function is, however, always located at zero. In the
present work, to further improve the learning performance, we propose the
concept of multi-kernel correntropy (MKC), in which each component of the
mixture Gaussian kernel can be centered at a different location. The properties
of the MKC are investigated and an efficient approach is proposed to determine
the free parameters in MKC. Experimental results show that the learning
algorithms under the maximum multi-kernel correntropy criterion (MMKCC) can
outperform those under the original maximum correntropy criterion (MCC) and the
maximum mixture correntropy criterion (MMCC).Comment: 10 pages, 5 figure
Two improved normalized subband adaptive filter algorithms with good robustness against impulsive interferences
To improve the robustness of subband adaptive filter (SAF) against impulsive
interferences, we propose two modified SAF algorithms with an individual scale
function for each subband, which are derived by maximizing correntropy-based
cost function and minimizing logarithm-based cost function, respectively,
called MCC-SAF and LC-SAF. Whenever the impulsive interference happens, the
subband scale functions can sharply drop the step size, which eliminate the
influence of outliers on the tap-weight vector update. Therefore, the proposed
algorithms are robust against impulsive interferences, and exhibit the faster
convergence rate and better tracking capability than the sign SAF (SSAF)
algorithm. Besides, in impulse-free interference environments, the proposed
algorithms achieve similar convergence performance as the normalized SAF (NSAF)
algorithm. Simulation results have demonstrated the performance of our proposed
algorithms.Comment: 14 pages,8 figures,accepted by Circuits, Systems, and Signal
Processing on Feb 23, 201
Maximum Correntropy Adaptive Filtering Approach for Robust Compressive Sensing Reconstruction
Robust compressive sensing(CS) reconstruction has become an attractive
research topic in recent years. Robust CS aims to reconstruct the sparse
signals under non-Gaussian(i.e. heavy tailed) noises where traditional CS
reconstruction algorithms may perform very poorly due to utilizing norm
of the residual vector in optimization. Most of existing robust CS
reconstruction algorithms are based on greedy pursuit method or convex
relaxation approach. Recently, the adaptive filtering framework has been
introduced to deal with the CS reconstruction, which shows desirable
performance in both efficiency and reconstruction performance under Gaussian
noise. In this paper, we propose an adaptive filtering based robust CS
reconstruction algorithm, called regularized maximum correntropy
criterion(-MCC) algorithm, which combines the adaptive filtering framework
and maximum correntropy criterion(MCC). MCC has recently been successfully used
in adaptive filtering due to its robustness to impulsive non-Gaussian noises
and low computational complexity. We analyze theoretically the stability of the
proposed -MCC algorithm. A mini-batch based -MCC(MB--MCC)
algorithm is further developed to speed up the convergence. Comparison with
existing robust CS reconstruction algorithms is conducted via simulations,
showing that the proposed -MCC and MB--MCC can achieve significantly
better performance than other algorithms
State Estimation-Based Robust Optimal Control of Influenza Epidemics in an Interactive Human Society
This paper presents a state estimation-based robust optimal control strategy
for influenza epidemics in an interactive human society in the presence of
modeling uncertainties. Interactive society is influenced by the random
entrance of individuals from other human societies whose effects can be modeled
as a non-Gaussian noise. Since only the number of exposed and infected humans
can be measured, states of the influenza epidemics are first estimated by an
extended maximum correntropy Kalman filter (EMCKF) to provide a robust state
estimation in the presence of the non-Gaussian noise. An online quadratic
program (QP) optimization is then synthesized subject to a robust control
Lyapunov function (RCLF) to minimize susceptible and infected humans, while
minimizing and bounding the rates of vaccination and antiviral treatment. The
joint QP-RCLF-EMCKF meets multiple design specifications such as state
estimation, tracking, pointwise control optimality, and robustness to parameter
uncertainty and state estimation errors that have not been achieved
simultaneously in previous studies. The uniform ultimate boundedness
(UUB)/convergence of error trajectories is guaranteed using a Lyapunov
stability argument. The soundness of the proposed approach is validated on the
influenza epidemics of an interactive human society with a population of 16000.
Simulation results show that the QP-RCLF-EMCKF achieves appropriate tracking
and state estimation performance. The robustness of the proposed controller is
finally illustrated in the presence of modeling error and non-Gaussian noise
Robust Dynamic CPU Resource Provisioning in Virtualized Servers
We present robust dynamic resource allocation mechanisms to allocate
application resources meeting Service Level Objectives (SLOs) agreed between
cloud providers and customers. In fact, two filter-based robust controllers,
i.e. H-infinity filter and Maximum Correntropy Criterion Kalman filter
(MCC-KF), are proposed. The controllers are self-adaptive, with process noise
variances and covariances calculated using previous measurements within a time
window. In the allocation process, a bounded client mean response time (mRT) is
maintained. Both controllers are deployed and evaluated on an experimental
testbed hosting the RUBiS (Rice University Bidding System) auction benchmark
web site. The proposed controllers offer improved performance under abrupt
workload changes, shown via rigorous comparison with current state-of-the-art.
On our experimental setup, the Single-Input-Single-Output (SISO) controllers
can operate on the same server where the resource allocation is performed;
while Multi-Input-Multi-Output (MIMO) controllers are on a separate server
where all the data are collected for decision making. SISO controllers take
decisions not dependent to other system states (servers), albeit MIMO
controllers are characterized by increased communication overhead and potential
delays. While SISO controllers offer improved performance over MIMO ones, the
latter enable a more informed decision making framework for resource allocation
problem of multi-tier applications.Comment: 16 page
Outlier-robust Kalman filters with mixture correntropy
We consider the robust filtering problem for a nonlinear state-space model
with outliers in measurements. To improve the robustness of the traditional
Kalman filtering algorithm, we propose in this work two robust filters based on
mixture correntropy, especially the double-Gaussian mixture correntropy and
Laplace-Gaussian mixture correntropy. We have formulated the robust filtering
problem by adopting the mixture correntropy induced cost to replace the
quadratic one in the conventional Kalman filter for measurement fitting errors.
In addition, a tradeoff weight coefficient is introduced to make sure the
proposed approaches can provide reasonable state estimates in scenarios where
measurement fitting errors are small. The formulated robust filtering problems
are iteratively solved by utilizing the cubature Kalman filtering framework
with a reweighted measurement covariance. Numerical results show that the
proposed methods can achieve a performance improvement over existing robust
solutions
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