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An Improved Least Stochastic Entropy Algorithm for Strong Noncircular Inputs and Noise
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DOI：10.1109/LSP.2022.3163966.png)
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Abstract
En 中文
When the noise is second-order (SO) noncircular, the least stochastic entropy (LSE) algorithm can obtain a low steady-state misalignment compared to the complex-valued least mean-square (CLMS) algorithm. However, the convergence rate of LSE will decrease if the input of the adaptive filter is SO noncircular. This letter analyzes the cause of this problem and proposes an improved LSE (ILSE), which uses a combination strategy to accelerate its convergence rate. To predict its stochastic behavior, the performance of ILSE is analyzed. Simulation results are provided to verify the effectiveness of ILSE and the accuracy of the theoretical analysis.
Keywords:
Signal processing algorithms
Convergence
Steady-state
Noise measurement
Entropy
Prediction algorithms
Covariance matrices
Second-order noncircular noise
least stochastic entropy (LSE) algorithm
performance analysis
Journal
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