1 citations · 3 across the 15 of their papers we have counts for
6 papers · 2 filters
Outlier-Robust unscented Kalman filter based on generalized correntropy induced
Jinhui Hu, Haiquan Zhao, Yi Peng
Conventional Kalman filtering (KF) approaches exhibit significant limitations in addressing nonlinear state estimation problems contaminated by non-Gaussian noise disturbances. To…
Decentralized Variational Bayesian UKF with Maximum Generalized Student's t-kernel Correntropy for Wide-Area Power System state estimation
Jinhui Hu, Haiquan Zhao, Yi Peng
A Conventional centralized state estimators exhibit limited robustness in large-scale grids and face practical deployment hurdles. To overcome these challenges, this paper proposes…
Broad learning system with robust adaptive kernel
Haiquan Zhao, Jinhui Hu, Xin Lua
For the performance degradation problem of broad learning system (BLS) in non-Gaussian noise environment, the variant of BLS based on M-estimator shows good robust performance. How…
A Fast Robust Adaptive filter using Improved Data-Reuse Method
Yi Peng, Haiquan Zhao, Jinhui Hu
Adaptive filter in complex scenarios demands algorithms that integrate fast convergence, low complexity, and robust performance under diverse noise conditions. To address this chal…
Robust distributed extended Kalman filter based on adaptive multi-kernel mixture maximum correntropy for non-Gaussian systems
Duc Viet Nguyen, Haiquan Zhao, Jinhui Hu +1
As one of the most advanced variants in the correntropy family, the multi-kernel correntropy criterion demonstrates superior accuracy in handling non-Gaussian noise, particularly w…
P-norm based Fractional-Order Robust Subband Adaptive Filtering Algorithm for Impulsive Noise and Noisy Input
Jianhong Ye, Haiquan Zhao, Yi Peng
Building upon the mean p-power error (MPE) criterion, the normalized subband p-norm (NSPN) algorithm demonstrates superior robustness in -stable noise environments ($1 < α\leq 2…