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Double-Proportionate Uncertainty-Aware Diffusion Algorithm for Distributed Estimation
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DOI：10.1109/TCSII.2023.3323871.png)
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Abstract
En 中文
In this brief, we consider an uncertainty in the linear measurement model of the distributed estimation problem. To deal with this uncertainty, the uncertainty vector is estimated along with the unknown vector itself. Hence, the cost function is assumed to be dependent on both unknown vector and uncertainty vector and these two vectors are separately estimated in an iterative manner. First, we update the unknown vector assuming uncertainty vector is known and then, we update the uncertainty vector assuming the unknown vector is given. Then, to achieve lower error, we consider the double proportionate scheme in the uncertainty-aware algorithm. Two diagonal gain matrices are obtained mathematically with one degree of freedom, i.e., one gain matrix is obtained in closed-form assuming the other gain matrix is known. Simulation results demonstrate superior performance of the proposed algorithms as compared to the classical diffusion least mean square (LMS) algorithm and achieve near the performance of a case without uncertainty under extreme noise conditions.
Keywords:
Uncertainty
Estimation
Cost function
Measurement uncertainty
Mathematical models
Indexes
Adaptation models
Distributed estimation
proportionate
uncertainty
diffusion LMS
Journal
I
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