Abstract
Distributed estimation over sensor networks has attracted much attention due to its various applications. The mean-square error (MSE) criterion is one of the most popular cost functions used in distributed estimation, which achieves its optimality only under Gaussian noise. However, impulsive noise also widely exists in real-world sensor networks. Thus, the distributed estimation algorithm based on the minimum kernel risk-sensitive loss (MKRSL) criterion is proposed in this paper to deal with non-Gaussian noise, particularly for impulsive noise. Furthermore, multiple tasks estimation problems in sensor networks are considered. Differing from a conventional single-task, the unknown parameters (tasks) can be different for different nodes in the multitask problem. Another important issue we focus on is the impact of the task similarity among nodes on multitask estimation performance. Besides, the performance of mean and mean square are analyzed theoretically. Simulation results verify a superior performance of the proposed algorithm compared with other related algorithms.
Highlights
Distributed data processing over sensor networks has emerged as an attractive and challenging research area for various applications such as industrial automation, cognitive radios and inference tasks [1,2,3,4]
With the mean-square error (MSE) criterion, these algorithms can accomplish a satisfying performance in a Gaussian noise environment
The value of the parameters α and λ for D-generalized maximum correntropy criterion (GMCC) are selected to achieve the best performance in both the Gaussian and impulsive noise environments
Summary
Xinyu Li 1,2,† , Qing Shi 2 , Shuangyi Xiao 2 , Shukai Duan 1,2 and Feng Chen 1,2, *,†. Current address: Chongqing Collaborative Innovation Center for Brain Science, Southwest University, Chongqing 400715, China
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