基于手机端GNSS和音频信号的室内/室外导航场景感知模型构建与优化方法

doi:10.13474/j.cnki.11-2246.2025.0218

测绘通报 ›› 2025, Vol. 0 ›› Issue (2): 101-107.doi: 10.13474/j.cnki.11-2246.2025.0218

基于手机端GNSS和音频信号的室内/室外导航场景感知模型构建与优化方法

李昂¹, 张学东², 马悦³, 杨振⁴, 李林阳^4,5

1. 61206部队, 北京 100000;
2. 31457部队, 辽宁沈阳 110000;
3. 96861部队, 河南洛阳 471600;
4. 信息工程大学地理空间信息学院, 河南郑州 450001;
5. 地理信息工程国家重点实验室, 陕西西安 710054

收稿日期:2024-06-13 发布日期:2025-03-03
通讯作者: 李林阳。E-mail:linyangli@whu.edu.cn
作者简介:李昂(2002—),女,助理工程师,研究方向为测量数据处理理论与方法。E-mail:1327978613@qq.com
基金资助:
国家自然科学基金(42474043;42104003);地理信息工程国家重点试验室开放基金(SKLGIE2023-Z-2-1);博士后科学基金(2022M712442);重庆市自然科学基金(CSTB2022NSCQ-MSX1129)

Establishment and optimization method of indoor/outdoor navigation context perception model based on GNSS and audio signals on the mobile phone

LI Ang¹, ZHANG Xuedong², MA Yue³, YANG Zhen⁴, LI Linyang^4,5

1. Troops 61206, Beijing 100000, China;
2. Troops 31457, Shenyang 110000, China;
3. Troops 96861, Luoyang 471600, China;
4. Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China;
5. State Key Laboratory of Geo-Information Engineering, Xi'an 710054, China

Received:2024-06-13 Published:2025-03-03

摘要/Abstract

摘要： 当前,基于机器学习和智能手机的室内/室外导航场景感知模型存在仅利用室外GNSS信号和感知精度差的问题。我国自主可控的音频定位技术的推广,为室内/室外导航场景感知提供了新的可用信号。本文优化选取手机端12个GNSS和音频信号的特征,设计基于鲸鱼优化(WOA)和随机森林(RF)的室内/室外导航场景感知模型。结果表明,相较于单一特征,音频信号特征和GNSS信号特征组合显著提高了场景感知的精度;与反向传播神经网络(BPNN)、卷积神经网络(CNN)、支持向量机(SVM)、长短时记忆网络(LSTM)、RF 5种传统方法相比,本文所提方法的效果最优,准确度、精确度、召回率、F1均超过了96%,本文所提算法与传统RF算法的计算速度基本相当。

关键词: 室内/室外导航场景感知, GNSS和音频信号, 智能手机, 随机森林, 鲸鱼优化

Abstract: The existing indoor/outdoor navigation context perception models based on machine learning and smart phones have the problems with only utilizing GNSS signals and poor perception accuracy. With the promotion of China's independent and controllable audio positioning technology, new available signals have been provided for that. In this paper, 12 GNSS and audio signal features on the mobile phone are selected, then an indoor/outdoor navigation context perception model based on whale optimization algorithm (WOA) and random forest (RF) is designed. The result shows that compared with only using audio signal features and GNSS signal features, the accuracy of context perception is significantly improved. Compared with the five traditional methods of back propagation neural network (BPNN), convolutional neural network (CNN), support vector machine (SVM), long short term memory (LSTM), and RF, the results of the proposed method are the best, accuracy, precision, recall rate and F1 are all exceeding 96%. The calculation speed of the proposed algorithm is basically equivalent to that of traditional RF.

Key words: indoor/outdoor navigation context detection, GNSS and audio signals, smart phone, random forest, whale optimization algorithm

中图分类号:

P228

李昂, 张学东, 马悦, 杨振, 李林阳. 基于手机端GNSS和音频信号的室内/室外导航场景感知模型构建与优化方法[J]. 测绘通报, 2025, 0(2): 101-107.

LI Ang, ZHANG Xuedong, MA Yue, YANG Zhen, LI Linyang. Establishment and optimization method of indoor/outdoor navigation context perception model based on GNSS and audio signals on the mobile phone[J]. Bulletin of Surveying and Mapping, 2025, 0(2): 101-107.

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基于手机端GNSS和音频信号的室内/室外导航场景感知模型构建与优化方法

Establishment and optimization method of indoor/outdoor navigation context perception model based on GNSS and audio signals on the mobile phone

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