HUID: DBN-based fingerprint localization and tracking system with hybrid UWB and IMU
HUID: DBN-based fingerprint localization and tracking system with hybrid UWB and IMU
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DOI：
10.23919/jcc.2023.02.008
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发表时间：
2023-02
影响因子：
4.1
通讯作者：
Junchang Sun;Rongyan Gu;Shiyin Li;Shuai Ma;Hongmei Wang;Zongyan Li;Weizhou Feng
中科院分区：
文献类型：
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作者：
Junchang Sun;Rongyan Gu;Shiyin Li;Shuai Ma;Hongmei Wang;Zongyan Li;Weizhou Feng
在恶劣的室内环境中，高精度定位技术受到了广泛的关注。本文提出了一种基于深度信任网络的指纹定位与跟踪系统来估计标签的位置。在该系统中，我们提出用系数作为指纹，将超宽带(UWB)估计和惯性测量单元(IMU)估计线性结合起来，称为HUID系统。具体地说，指纹由DBN训练并由径向基函数(RBF)估计。然而，基于超宽带的三边估计方法受到非视距(NLOS)问题的严重影响，限制了定位精度。为了解决这一问题，我们采用随机森林分类器来识别视线(LOS)和非视距条件。然后，为了提高超宽带定位精度，在识别结果的基础上，采用随机森林回归来减小测距误差。实验结果表明，与现有的扩展卡尔曼滤波(EKF)、单UWB和单IMU估计方法相比，所提HUID系统定位误差的均方误差(MSE)分别降低了12.96%、50.16%和64.92%。
High-precision localization technology is attracting widespread attention in harsh indoor environments. In this paper, we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network (DBN). In this system, we propose using coefficients as fingerprints to combine the ultra-wideband (UWB) and inertial measurement unit (IMU) estimation linearly, termed as a HUID system. In particular, the fingerprints are trained by a DBN and estimated by a radial basis function (RBF). However, UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight (NLoS) problem, which limits the localization precision. To tackle this problem, we adopt the random forest classifier to identify line-of-sight (LoS) and NLoS conditions. Then, we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision. The experimental results show that the mean square error (MSE) of the localization error for the proposed HUID system reduces by 12.96%, 50.16%, and 64.92% compared with that of the existing extended Kalman filter (EKF), single UWB, and single IMU estimation methods, respectively.