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Diffusion Quantized Recursive Mixture Minimum Error Entropy Algorithm
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AI
DOI：10.1109/TCSII.2022.3199246.png)
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Abstract
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The minimum error entropy (MEE) criterion is widely used in distributed estimation, since it is insensitive to many types of non-Gaussian noises. However, the default Gaussian kernel function may not always be a proper kernel function. To solve this problem and further improve the performance of the diffusion recursive MEE (DRMEE) algorithm, a diffusion recursive mixture MEE (DRMMEE) algorithm is proposed by combining the mixture MEE criterion and the diffusion strategy. In addition, a quantized version of DRMMEE called the diffusion quantized recursive mixture MEE (DQRMMEE) algorithm, is proposed to reduce the computational burden of DRMMEE. Simulation results show that DRMMEE has higher filtering accuracy than other recursive least-squares-based algorithms, and DQRMMEE even has similar filtering accuracy to DRMMEE in different non-Gaussian noise environments.
Keywords:
Distributed estimation
mixture minimum error entropy
recursive least squares
adaptive networks
quantization
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4.9
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