Abstract
In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in a distributed manner. This paper proposed a robust diffusion estimation algorithm based on a minimum error entropy criterion with a self-adjusting step-size, which are referred to as the diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has a fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS algorithm with the diffusion minimum error entropy (DMEE) algorithm, an Improving DMEE-SAS algorithm is proposed for a non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms.
Highlights
The problem of parameter estimation, which is the indirect determination of the unknown parameters from measurements of other quantities [1,2,3,4,5,6], is a key issue in the signal processing field
We introduce an minimum error entropy (MEE) criterion, which could be used to derive a robust diffusion estimation algorithm with a self-adjusting step-size (DMEE-SAS) algorithm
For the purpose of clarity, we summarize the procedure of the Improving diffusion minimum error entropy (DMEE)-SAS algorithm in Algorithm 2
Summary
The problem of parameter estimation, which is the indirect determination of the unknown parameters from measurements of other quantities [1,2,3,4,5,6], is a key issue in the signal processing field. The efforts are mainly directed at searching for a more robust cost function to replace the MSE criterion, which is optimal only when the measurement noise is Gaussian To address this problem, the diffusion least mean p-power (DLMP). The DMEE algorithm achieved improved performance for non-Gaussian noise with the fixed step-size, but it still suffers from conflicting requirements between convergence rate and the steady-state mean square error. To the best of our knowledge, the variable step-size technique has not been extended to the field of distributed minimum error entropy estimation for non-Gaussian noise yet. We incorporate the minimum error entropy criterion with self-adjusting step-size (MEE-SAS) [42] into the cost function in diffusion distributed estimation.
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