.png){kind=icon}
返回
Complex-Valued adaptive networks based on entropy estimation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2018.03.003.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In distributed estimation, the mean-square error (MSE) criterion has been extensively studied. When complex-valued signals are involved, the additive noise can present different degrees of non-circular properties. The MSE criterion can be optimal only when the error signal is circular, and may not perform well for non-circular error signal. To improve the performance, we present a new diffusion adaptive strategy using the Gaussian entropy criterion as the cost function. Complex-valued Gaussian entropy was early introduced for linear and widely linear filtering. Unfortunately, the closed-form solution based on Gaussian entropy was not obtained due to the nonlinearity of the entropy equation. In this paper, we derive a closed-form solution based on Gaussian entropy for linear and widely linear filters, and provide mean value steady and mean-square performance analysis for the network in detail. Our theoretical analysis demonstrates that the steady-state error approaches zero when the additive noise is maximally non-circular. The simulations demonstrate that the proposed method outperforms the MSE criterion for non-circular noise. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Gaussian entropy
Multi-sensor network (MSN)
Mean-square error (MSE)
Degree of non-circularity (DNC)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
机构
引用论文
.png){kind=icon}
Magnetic state of 10–40 Ma old ocean basalts and its implications for natural remanent magnetization
PREAI
分享收藏
查看更多内容