HUID: DBN-based fingerprint localization and tracking system with hybrid UWB and IMU

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.