Conjugate Gradient Adaptive Learning with Tukey's Biweight M-Estimate
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Abstract (arXiv)
We propose a novel M-estimate conjugate gradient (CG) algorithm, termed Tukey's biweight M-estimate CG (TbMCG), for system identification in impulsive noise environments. In particular, the TbMCG algorithm can achieve a faster convergence while retaining a reduced computational complexity as compared to the recursive least-squares (RLS) algorithm. Specifically, the Tukey's biweight M-estimate incorporates a constraint into the CG filter to tackle impulsive noise environments. Moreover, the convergence behavior of the TbMCG algorithm is analyzed. Simulation results confirm the excellent performance of the proposed TbMCG algorithm for system identification and active noise control applications.
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References · 7
- 2016Design of Robust Adaptive Beamforming Algorithms Based on Low-Rank and Cross-Correlation Techniquescited by 20
- 2014Direction Finding Algorithms with Joint Iterative Subspace Optimizationcited by 19
- 2014Sparsity-Aware Adaptive Algorithms Based on Alternating Optimization with Shrinkagecited by 15
- 2018Subspace-Orbit Randomized Decomposition for Low-rank Matrix Approximationcited by 10
- 2018Study of Robust Diffusion Recursive Least Squares Algorithms with Side Information for Networked Agentscited by 10
- 2014Kernel Least Mean Square with Adaptive Kernel Sizecited by 4
- 2021Robust Adaptive Filtering Based on Exponential Functional Link Networkcited by 3
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