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面向室内地下遮蔽空间的定位可信性提升方法

易卿武 黄璐 蔚保国 廖桂生

易卿武, 黄璐, 蔚保国, 廖桂生. 面向室内地下遮蔽空间的定位可信性提升方法[J]. 电子与信息学报, 2025, 47(5): 1529-1542. doi: 10.11999/JEIT240870
引用本文: 易卿武, 黄璐, 蔚保国, 廖桂生. 面向室内地下遮蔽空间的定位可信性提升方法[J]. 电子与信息学报, 2025, 47(5): 1529-1542. doi: 10.11999/JEIT240870
YI Qingwu, HUANG Lu, YU Baoguo, LIAO Guisheng. Methods for Enhancing Positioning Reliability in Indoor and Underground Satellite-shielded Environments[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1529-1542. doi: 10.11999/JEIT240870
Citation: YI Qingwu, HUANG Lu, YU Baoguo, LIAO Guisheng. Methods for Enhancing Positioning Reliability in Indoor and Underground Satellite-shielded Environments[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1529-1542. doi: 10.11999/JEIT240870

面向室内地下遮蔽空间的定位可信性提升方法

doi: 10.11999/JEIT240870
基金项目: 城市遮蔽空间超宽带稳健厘米级定位关键技术(D2024523007),卫星遮蔽空间多传感器智能融合定位研究(F2024523004)
详细信息
    作者简介:

    易卿武:男,研究员,研究方向为北斗综合PNT,完好性技术

    黄璐:男,博士,研究方向为室内定位,多源融合

    蔚保国:男,研究员,研究方向为综合PNT,北斗卫星导航

    廖桂生:男,教授,研究方向为雷达信号处理,多维信号处理

    通讯作者:

    黄璐 hlcetc54@163.com

  • 中图分类号: TN96

Methods for Enhancing Positioning Reliability in Indoor and Underground Satellite-shielded Environments

Funds: Key Technologies for Robust Centimeter-Level Ultra-Wideband Positioning in Urban Obstructed Environments (D2024523007), Research on Multi-Sensor Intelligent Fusion Positioning in Satellite-Denied Environments (F2024523004)
  • 摘要: 该文提出一种无监督自编码器及非线性滤波结合的室内定位可信性提升方法,设计了深度卷积神经网络辅助的降噪变分自编码器模型(DVAE-CNN),分别从量测数据质量评估、目标状态转移方程表征以及环境先验信息辅助的权重更新策略多方面调控定位结果,克服复杂室内环境下由于信息丢失、出错、扰动等因素带来的定位可信性低的问题,相比未增加可信调控机制的定位结果平均定位精度提升了74.6%,定位可靠性提高了88.2%。最后,在2022北京冬奥会体育场馆内进行了大量试验,结果表明所提方法能够提供高鲁棒、高可信、高连续的位置服务能力,具有较大的应用及推广价值。
  • 图  1  顾及多层级可信约束的室内定位系统框架

    图  2  复杂概率分布组成示意图

    图  3  DVAE-CNN模型结构及预训练过程

    图  4  基于重构概率的多维数据可信评估模型

    图  5  PDR算法示意图

    图  6  行人运动过程中加速度信息的变化情况

    图  7  地理先验信息辅助位置可信约束

    图  8  实验环境图

    图  9  可信评估机制有效性分析结果

    图  10  定位误差分析

    图  11  北京2022冬奥会场馆(雪如意)内部结构

    图  12  冬奥会场馆看台北区一楼测试环境及定位结果对比

    图  13  冬奥会场馆看台南区一楼测试环境及定位结果对比

    1  多源数据可信评估模型训练与使用

     输入:多源数据X;可信评估阈值α
     输出:评估模型 B
     (1) 数据预处理(数据规范化、输入数据拦截、添加噪声、数据级
       别划分,包括训练、测试和验证数据);
     (2) while 编码器模型训练 do
     (3) 初始化DVAE-CNN模型并设置超参数;
     (4) 载入训练数据集;
     (5)  得到均值和方差以及样本的隐含特征$ z $;
     (6)  通过解码器获取重构数据;
     (7)  通过比较重构数据和原始数据得到重构误差e
     (8)  确定模型是否已收敛到设置的阈值,如果满足收敛条件,
        则设置提前停止机制以结束训练。如果没有,则转到步
        骤(6);
     (9) 微调网络并更新参数,重复步骤(4)~(6),直到模型收敛;
     (10) 保存模型参数;
     (11) 使用$ \eta $来评估$ t $时刻图谱$ {x_{(t)}} $;
     (12) while 执行新的测量值 do
     (13) if 重构误差e小于可信阈值$ \alpha $then
     (14)  输入到定位模型;
     (15) else剔除当前时刻的数据,并重复步骤(12);end if.
    下载: 导出CSV

    2  位置可信性提升方法

     输入:先验地图信息;粒子数量$ n $,粒子步长$ L $,可信评估阈值
     $ \alpha $;重采样阈值$ \tau $;
     输出:可信定位结果$ P $。
     (1) 初始化:从初始状态分布中采样一组粒子;
     (2) while 定位结果 do
     (3) for 每一个粒子 do
     (4) 通过式(17)更新当前位置;
     (5) 按公式$ \omega = \dfrac{1}{{\sqrt {2{\pi}} {\sigma _\omega }}}\exp \left\{ { - \dfrac{{{{\left| {s - P'} \right|}^2}}}{{2{\sigma _\omega }^2}}} \right\} $更新权重信息, 其
       中$ s $是当前时刻的粒子状态,$ {\sigma _\omega } $是测量偏差。
     (6) if 粒子穿过墙壁(或建筑结构)then
     (7)  将相应粒子的权重设置为0,即消除可能的异常位置;
     (8) end if
     (9)  if 粒子数小于$ \tau $then
     (10) 重采样:基于权重生成新粒子(多项式重采样);
     (11) end if
     (12) 通过粒子状态和权重获得当前定位结果;
     (13) end for
     (14) end while
    下载: 导出CSV

    表  1  DVAE-CNN模型超参数

    超参数设置 数值设置
    输入数据尺寸 25×25(根据地图确定)
    卷积层 3×3 filter size, stride = 2
    latent_dim 20
    激活函数 ReLU(Rectified Liner Unit)
    卷积层数 2
    Pooling Size 2
    Dropout 0.5
    全连接层数 2
    优化器 Adam
    学习率 0.000 1
    Batch Size 32
    Epochs 500(EarlyStopping, patience = 10,
    verbose=1)
    下载: 导出CSV

    表  2  冬奥场馆多场景定位性能统计(m)

    测试区域 参考位置 定位结果 X误差 Y误差
    X Y X Y X Y
    看台北区 134.25
    141.57
    137.49
    43.12
    35.98
    51.15
    134.19
    141.96
    137.85
    43.20
    34.95
    52.66
    0.06
    0.39
    0.36
    0.08
    1.03
    0.51
    看台南区 12.45
    7.68
    18.22
    17.50
    15.39
    19.10
    11.93
    7.05
    18.52
    16.32
    15.01
    17.86
    0.52
    0.63
    0.30
    1.18
    0.38
    1.24
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-10-15
  • 修回日期:  2025-04-16
  • 网络出版日期:  2025-05-06
  • 刊出日期:  2025-05-01

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