Results 1 to 10 of about 9,957 (160)
Random Weighting-Based Nonlinear Gaussian Filtering
IEEE Access, 2020 The Gaussian filtering is a commonly used method for nonlinear system state estimation. However, this method requires both system process noise and measurement noise to be white noise sequences with known statistical characteristics.
Zhaohui Gao +4 more
doaj +4 more sources
Gaussian filters for nonlinear filtering problems [PDF]
IEEE Transactions on Automatic Control, 2000 The most widely used filter to estimate the state of a nonlinear stochastic system from noisy observation data is the extended Kalman filter. However, if the nonlinearities are significant, its performance can be considerably improved as recent works by Alspace and Sorenson (1967, 1972), C. P. Fang, Julier and Uhlmann (1994, 1995) have shown.
K Xiong
exaly +4 more sources
Gaussian Filtering for Simultaneously Occurring Delayed and Missing Measurements [PDF]
IEEE Access, 2022 Approximate filtering algorithms in nonlinear systems assume Gaussian prior and predictive density and remain popular due to ease of implementation as well as acceptable performance. However, these algorithms are restricted by two major assumptions: they
Amit Kumar Naik +4 more
doaj +2 more sources
Gaussian sum particle filtering [PDF]
IEEE Transactions on Signal Processing, 2003 We use the Gaussian particle filter to build several types of Gaussian sum particle filters. These filters approximate the filtering and predictive distributions by weighted Gaussian mixtures and are basically banks of Gaussian particle filters. Then, we extend the use of Gaussian particle filters and Gaussian sum particle filters to dynamic state ...
Petar M Djuric
exaly +2 more sources
Cognitive Computation and Systems, 2022 Aiming at meetiing the need to filtering flight trajectory data for aircraft testing, a novel adaptive cubature Kalman filter (CKF) is proposed based on the maximum correntropy and Gaussian‐sum in this paper.
Jing G. Bai +4 more
doaj +1 more source
Nonlinear Gaussian Filter with Multi-Step Colored Noise
Actuators, 2022 Color noise is a special kind of noise often occurring in localization systems, and it is more suitable than the general Gaussian white noise to model time dependence due to time delay or high-frequency sampling.
Yidi Teng, Shouzhao Sheng, Yubin Zheng
doaj +1 more source
Split-Gaussian particle filter [PDF]
2015 23rd European Signal Processing Conference (EUSIPCO), 2015 Publication in the conference proceedings of EUSIPCO, Nice, France ...
Juho Kokkala, Simo Särkkä
openaire +1 more source
Sensing Tensors With Gaussian Filters [PDF]
IEEE Transactions on Information Theory, 2017 Sparse recovery from linear Gaussian measurements has been the subject of much investigation since the breaktrough papers \cite{CRT:IEEEIT06} and \cite{donoho2006compressed} on Compressed Sensing. Application to sparse vectors and sparse matrices via least squares penalized with sparsity promoting norms is now well understood using tools such as ...
Stéphane Chrétien, Tianwen Wei
openaire +2 more sources
Wrapped Particle Filtering for Angular Data
IEEE Access, 2022 Particle filtering is probably the most widely accepted methodology for general nonlinear filtering applications. The performance of a particle filter critically depends on the choice of proposal distribution.
Guddu Kumar +4 more
doaj +1 more source
IEEE Access, 2020 This paper concerns the nonlinear filter designing methods in the information space of the nonlinear systems with non-Gaussian noises. Firstly, the prediction information vector is obtained by the traditional square root cubature information filtering ...
Xiaoliang Feng +4 more
doaj +1 more source