A Kernel Affine Projection-Like Algorithm in Reproducing Kernel Hilbert Space

Wu, Qishuai; Li, Yingsong; Zakharov, Yuriy; Xue, Wei; Shi, Wanlu

doi:10.1109/tcsii.2019.2947317

Cited by 23 publications

(8 citation statements)

References 22 publications

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“…In order to evaluate the proposed RBPF with variational inference, we use the following modified benchmark model [6] for the illustrations is the uniform distribution [13], [36]. We compare the proposed approach with the interacting multiple model (IMM) algorithm [1] and the APE filter [31].…”

Section: A Illustrative Examplementioning

confidence: 99%

A Rao-Blackwellized Particle Filter With Variational Inference for State Estimation With Measurement Model Uncertainties

Cheng

Tourneret

2020

IEEE Access

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This paper develops a Rao-Blackwellized particle filter with variational inference for jointly estimating state and time-varying parameters in non-linear state-space models (SSM) with non-Gaussian measurement noise. Depending on the availability of the conjugate prior for the unknown parameters, the joint posterior distribution of the state and unknown parameters is approximated by using an auxiliary particle filter with a probabilistic changepoint model. The distribution of the SSM parameters conditionally on each particle is then updated by using variational Bayesian inference. Experiments are first conducted on a modified nonlinear benchmark model to compare the performance of the proposed approach with other state-of-the-art approaches. Finally, in the context of GNSS multipath mitigation, the proposed approach is evaluated based on data obtained from a measurement campaign conducted in a street urban canyon. INDEX TERMS Joint state and parameter estimation, Rao-blackwellized particle filter, state-space models, variational inference.

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Section: A Illustrative Examplementioning

confidence: 99%

A Rao-Blackwellized Particle Filter With Variational Inference for State Estimation With Measurement Model Uncertainties

Cheng

Tourneret

2020

IEEE Access

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“…The kernel adaptive algorithm (KAA) has been developed for several years based on the least-mean-squares scheme and has been used for non-linear channel equalization [17][18][19][20][21][22][23] and tree pest prediction. The kernel adaptive filter (KAF) has had much more attention paid to it, which is always presented for non-linear predictions [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

“…The kernel adaptive filter (KAF) has had much more attention paid to it, which is always presented for non-linear predictions [24][25][26]. As we know, the KAF is famous for its online learning ability derived in the reproducing kernel Hilbert space (RKHS) [17][18][19][20][21][22][23][24][25][26][27]. Recently, many KAF algorithms have been presented for non-linear signal processing [28,29], including the kernel-driven least mean squares (KLMS) [21], kernel-driven least mean fourth (KLMF) [27], and kernel-driven recursive least squares (KRLS) [28] based on the symmetry squared error function.…”

Section: Introductionmentioning

confidence: 99%

A Novel Second-OrderSine-Cost-Function-Derived Kernel Adaptive Algorithm for Non-Linear System Identification

Sun

Zhou

2023

Symmetry

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A novel kernel recursive second-order sine adaptive (KRSOSA) algorithm was devised for identifying non-linear systems, which was constructed using a symmetry squared sine function to develop a novel kernel loss function and recursive scheme. In the proposed KRSOSA algorithm, the squared sine function provides resistance to impulsive noise due to the sine operation, which was well-derived and investigated in the framework of kernel adaptive filtering (KAF). The behavior of the proposed KRSOSA algorithm was investigated and analyzed using computer simulations, which provided good performance for identifying non-linear systems under impulsive noises.

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“…Motivated by the studies in [27,28] on the Cauchy loss which has been successfully used in various robust learning applications, the multikernel minimum Cauchy kernel loss (MKMCKL) algorithm was reported in [29] showing the improved nonlinear filtering performance over counterpart single algorithm in the presence of extreme outliers. Recently, the kernel affine projection-like (KAPL) algorithm in RKHS was proposed and investigated for nonlinear channel equalization in scenarios of non-Gaussian noises [30]. e kernel least mean p-power (KLMP) algorithm was proposed to alleviate the adverse impact of impulsive noise in [31,32], independently.…”

Section: Introductionmentioning

confidence: 99%

Kernel Least Logarithmic Absolute Difference Algorithm

Wei

Shi

et al. 2022

Mathematical Problems in Engineering

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Kernel adaptive filtering (KAF) algorithms derived from the second moment of error criterion perform very well in nonlinear system identification under assumption of the Gaussian observation noise; however, they inevitably suffer from severe performance degradation in the presence of non-Gaussian impulsive noise and interference. To resolve this dilemma, we propose a novel robust kernel least logarithmic absolute difference (KLLAD) algorithm based on logarithmic error cost function in reproducing kernel Hilbert spaces, taking into account of the non-Gaussian impulsive noise. The KLLAD algorithm shows considerable improvement over the existing KAF algorithms without restraining impulsive interference in terms of robustness and convergence speed. Moreover, the convergence condition of KLLAD algorithm with Gaussian kernel and fixed dictionary is presented in the mean sense. The superior performance of KLLAD algorithm is confirmed by the simulation results.

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A Kernel Affine Projection-Like Algorithm in Reproducing Kernel Hilbert Space

Cited by 23 publications

References 22 publications

A Rao-Blackwellized Particle Filter With Variational Inference for State Estimation With Measurement Model Uncertainties

A Rao-Blackwellized Particle Filter With Variational Inference for State Estimation With Measurement Model Uncertainties

A Novel Second-OrderSine-Cost-Function-Derived Kernel Adaptive Algorithm for Non-Linear System Identification

Kernel Least Logarithmic Absolute Difference Algorithm

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