Azimuth estimation based on CNN and LSTM for geomagnetic and inertial sensors data

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS ICT Express Pub Date : 2024-06-01 DOI:10.1016/j.icte.2024.01.003

Jongtaek Oh , Sunghoon Kim

引用次数: 0

Abstract

Although estimating the azimuth using a geomagnetic sensor is very useful, the estimation error may be very large due to the surrounding geomagnetic disturbance. We proposed a novel method for preprocessing appropriately for geomagnetic and inertial sensor data to be suitable for the proposed Artificial Neural Network model and training method for the model. As a result, the probability of azimuth estimation error within 1 degree is 96.4% with regression estimation. For classification estimation, when the azimuth estimation probability is 90% or more, the probability that the azimuth estimation error is within 1 degree is 100%.

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基于 CNN 和 LSTM 的地磁和惯性传感器数据方位角估计

虽然使用地磁传感器估计方位角非常有用，但由于周围的地磁干扰，估计误差可能非常大。我们提出了一种新方法，对地磁和惯性传感器数据进行适当预处理，使其适合于所提出的人工神经网络模型和模型训练方法。结果，回归估计的方位角估计误差在 1 度以内的概率为 96.4%。对于分类估计，当方位角估计概率为 90% 或以上时，方位角估计误差在 1 度以内的概率为 100%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ICT Express Multiple-

CiteScore

10.20

自引率

1.90%

发文量

167

审稿时长

35 weeks

期刊介绍： The ICT Express journal published by the Korean Institute of Communications and Information Sciences (KICS) is an international, peer-reviewed research publication covering all aspects of information and communication technology. The journal aims to publish research that helps advance the theoretical and practical understanding of ICT convergence, platform technologies, communication networks, and device technologies. The technology advancement in information and communication technology (ICT) sector enables portable devices to be always connected while supporting high data rate, resulting in the recent popularity of smartphones that have a considerable impact in economic and social development.