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Euclidean Direction Search Algorithm Based on Maximum Correntropy Criterion
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AI
DOI：10.1109/LSP.2023.3301808.png)
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Abstract
En 中文
The Euclidean direction search (EDS) algorithm can reduce the complexity by avoiding the matrix inversion operation. However, it may fail to work in impulsive environments. To address this problem, a novel EDS based upon the maximum correntropy criterion (EDS-MCC) algorithm is proposed, which provides computational savings and robustness for combating impulsive noise. Additionally, the EDS-MCC algorithm is analyzed to obtain the theoretical performance by utilizing the energy conservation argument (ECA) and the Taylor expansion method. Simulations are exhibited to show the robustness of the EDS-MCC algorithm and verify the accuracy of the theoretical analysis.
Keywords:
Signal processing algorithms
Kernel
Filtering algorithms
Convergence
Taylor series
Steady-state
Signal to noise ratio
Maximum correntropy criterion
Euclidean direction search
impulsive noise
steady-state analysis
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W