A sensor data fusion algorithm based on suboptimal network powered deep learning

Ning Pan

doi:10.1016/j.aej.2021.12.058

2022-09-01

Ning Pan *

DOI：10.1016/j.aej.2021.12.058

原文链接

查看原文

摘要

En 中文

To improve the performance of data fusion in wireless sensor networks, based on the suboptimal network powered deep learning model, a data fusion algorithm that combines a stacked autoencoder (SAE) and a clustering protocol is proposed. The fusion algorithm for sensor data is investigated by suboptimal network powered deep learning to analyse the fusion processing at the sensor measurement level and the fusion processing at the localization result level. This paper establishes a suboptimal network powered deep learning model and derives its weighted least squares solution algorithm based on this model, also, the best linear unbiased estimation method for fusion of vibration sensor and vision sensor localization results is adopted. First, to overcome the filter divergence caused by rounding error, reduce the computational complexity, ensure the nonnegativity of the covariance, and improve the convergence speed of the filter, the target state volume after fusion using the proposed model is closer to the real state volume of the target compared to the state volume of each local sensor detection, verify the validity of the algorithm was increased. (c) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/

Keyword:

Suboptimal networks

Deep learning

Sensor data

Fusion algorithms

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

Alexandria Engineering Journal

IF:

6.8

论文数:

6.3K

被引数:

2.6W

机构

暂无机构信息

引用论文

A Triple-Blinded Randomized Trial Comparing the Hemostatic Effects of Large-Dose 10% Hydroxyethyl Starch 264/0.45 Versus 5% Albumin During Major Reconstructive Surgery

Anesthesia & Analgesia

IF0

2005-06-01

OAAI

Ramiro Arellano; Bing Siang Gan; Mary Jane Salpeter; Erik Yeo; Stuart McCluskey; Ruxandra Pinto; Jonathan Irish; Douglas C. Ross; D John Doyle; John Parkin; Dale Brown; Lorne Rotstein; Ian Witterick; Wayne Matthews; John Yoo; Peter C. Neligan; Pat Gullane; Howard Lampe

Study on stress corrosion cracking of aluminum alloys in marine atmosphere

Environment-Induced Cracking of Materials

IF0

2008-01-01

PREAI

Xiaoyun Zhang; Zhihua Sun; Zhihui Tang; Minghui Liu; Bin Li

A Distributed Approach for Wind Power Probabilistic Forecasting Considering Spatio-Temporal Correlation Without Direct Access to Off-Site Information

IEEE TRANSACTIONS ON POWER SYSTEMS

IF7.2

2018-09-01

PREAI

Zhang, Yao; Wang, Jianxue