Abstract
In this paper, we propose an algorithm for the estimation and compensation of second-order nonlinearity in Wireless Sensor Networks (WSNs) within a distributed estimation framework. First, we investigate the impact of second-order nonlinearity on the performance of the Diffusion Least Mean Square (DLMS) algorithm and derive an upper bound for the l2-norm of the error caused by nonlinearity. Next, we suggest a distributed algorithm incorporating additional nonlinearity estimation and compensation units. Furthermore, considering second-order nonlinearity, we calculate the Cramer-Rao Bound for estimating both the unknown vector and the nonlinearity coefficient vector, with the Fisher information matrix derived in a closed-form formula. Simulation results demonstrate the effectiveness of the proposed algorithm in enhancing the performance of distributed estimation in the presence of nonlinear sensors in a WSN.
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