An efficient robust adaptive filtering algorithm based on parallel subgradient projection techniques

2002 ◽  
Vol 50 (5) ◽  
pp. 1091-1101 ◽  
Author(s):  
I. Yamada ◽  
K. Slavakis ◽  
K. Yamada
Keyword(s):  
Adaptive Filtering ◽  
Filtering Algorithm ◽  
Subgradient Projection ◽  
Projection Techniques ◽  
Robust Adaptive

scholarly journals Robust Adaptive Filtering Algorithm for Self-Interference Cancellation with Impulsive Noise

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 196
Author(s):  
Jun Lu ◽  
Qunfei Zhang ◽  
Wentao Shi ◽  
Lingling Zhang ◽  
Juan Shi
Keyword(s):  
Adaptive Filtering ◽  
Interference Cancellation ◽  
Impulsive Noise ◽  
Weight Vector ◽  
Filtering Algorithm ◽  
Convergence Performance ◽  
Simulation Results ◽  
Robust Adaptive ◽  
Arctangent Cost Function ◽  
Simultaneous Transmission

Self-interference (SI) is usually generated by the simultaneous transmission and reception in the same system, and the variable SI channel and impulsive noise make it difficult to eliminate. Therefore, this paper proposes an adaptive digital SI cancellation algorithm, which is an improved normalized sub-band adaptive filtering (NSAF) algorithm based on the sparsity of the SI channel and the arctangent cost function. The weight vector is hardly updated when the impulsive noise occurs, and the iteration error resulting from impulsive noise is significantly reduced. Another major factor affecting the performance of SI cancellation is the variable SI channel. To solve this problem, the sparsity of the SI channel is estimated with the estimation of the weight vector at each iteration, and it is used to adjust the weight vector. Then, the convergence performance and calculation complexity are analyzed theoretically. Simulation results indicate that the proposed algorithm has better performance than the referenced algorithms.

scholarly journals Robust adaptive filtering algorithm based on maximum correntropy criteria for censored regression

2019 ◽  
Vol 160 ◽  
pp. 88-98 ◽  
Author(s):  
Wenyuan Wang ◽  
Haiquan Zhao ◽  
Kutluyıl Doğançay ◽  
Yi Yu ◽  
Lu Lu ◽  
...  
Keyword(s):  
Adaptive Filtering ◽  
Censored Regression ◽  
Filtering Algorithm ◽  
Robust Adaptive

Robust Adaptive Unscented Particle Filter

2013 ◽  
Vol 3 (2) ◽  
pp. 55-66 ◽  
Author(s):  
Li Xue ◽  
Shesheng Gao ◽  
Yongmin Zhong
Keyword(s):  
Particle Filter ◽  
Adaptive Filtering ◽  
Particle Filtering ◽  
Kinematic Model ◽  
Comparison Analysis ◽  
Unscented Transformation ◽  
Filtering Algorithm ◽  
Unscented Particle Filter ◽  
Observation Noise ◽  
Robust Adaptive

This paper presents a new robust adaptive unscented particle filtering algorithm by adopting the concept of robust adaptive filtering to the unscented particle filter. In order to prevent particles from degeneracy, this algorithm adaptively determines the equivalent weight function according to robust estimation and adaptively adjusts the adaptive factor constructed from predicted residuals to resist the disturbances of singular observations and the kinematic model noise. It also uses the unscented transformation to improve the accuracy of particle filtering, thus providing the reliable state estimation for improving the performance of robust adaptive filtering. Experiments and comparison analysis demonstrate that the proposed filtering algorithm can effectively resist disturbances due to system state noise and observation noise, leading to the improved filtering accuracy.

A Sparse Robust Adaptive Filtering Algorithm Based on the $q$-Rényi Kernel Function

2020 ◽  
Vol 27 ◽  
pp. 476-480
Author(s):  
Yiming Zhang ◽  
Libiao Peng ◽  
Xifeng Li ◽  
Yongle Xie
Keyword(s):  
Kernel Function ◽  
Adaptive Filtering ◽  
Filtering Algorithm ◽  
Robust Adaptive
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