139 research outputs found
Cubature Kalman filter Based on generalized minimum error entropy with fiducial point
In real applications, non-Gaussian distributions are frequently caused by
outliers and impulsive disturbances, and these will impair the performance of
the classical cubature Kalman filter (CKF) algorithm. In this letter, a
modified generalized minimum error entropy criterion with fiducial point
(GMEEFP) is studied to ensure that the error comes together to around zero, and
a new CKF algorithm based on the GMEEFP criterion, called GMEEFP-CKF algorithm,
is developed. To demonstrate the practicality of the GMEEFP-CKF algorithm,
several simulations are performed, and it is demonstrated that the proposed
GMEEFP-CKF algorithm outperforms the existing CKF algorithms with impulse
noise
Distributed fusion filter over lossy wireless sensor networks with the presence of non-Gaussian noise
The information transmission between nodes in a wireless sensor networks
(WSNs) often causes packet loss due to denial-of-service (DoS) attack, energy
limitations, and environmental factors, and the information that is
successfully transmitted can also be contaminated by non-Gaussian noise. The
presence of these two factors poses a challenge for distributed state
estimation (DSE) over WSNs. In this paper, a generalized packet drop model is
proposed to describe the packet loss phenomenon caused by DoS attacks and other
factors. Moreover, a modified maximum correntropy Kalman filter is given, and
it is extended to distributed form (DM-MCKF). In addition, a distributed
modified maximum correntropy Kalman filter incorporating the generalized data
packet drop (DM-MCKF-DPD) algorithm is provided to implement DSE with the
presence of both non-Gaussian noise pollution and packet drop. A sufficient
condition to ensure the convergence of the fixed-point iterative process of the
DM-MCKF-DPD algorithm is presented and the computational complexity of the
DM-MCKF-DPD algorithm is analyzed. Finally, the effectiveness and feasibility
of the proposed algorithms are verified by simulations
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
One-step condensed forms for square-root maximum correntropy criterion Kalman filtering
This paper suggests a few novel Cholesky-based square-root algorithms for the
maximum correntropy criterion Kalman filtering. In contrast to the previously
obtained results, new algorithms are developed in the so-called {\it condensed}
form that corresponds to the {\it a priori} filtering. Square-root filter
implementations are known to possess a better conditioning and improved
numerical robustness when solving ill-conditioned estimation problems.
Additionally, the new algorithms permit easier propagation of the state
estimate and do not require a back-substitution for computing the estimate.
Performance of novel filtering methods is examined by using a fourth order
benchmark navigation system example
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