Two-Stage Weighted Filtering Algorithm for Seamless Positioning in Indoor–Outdoor Crossover Areas

Indoor and outdoor seamless higher precision positioning has recently become a research hotspot. Heterogeneous sensor fusion technology has emerged as a popular approach to achieving seamless positioning technology. However, the problem of ping-pong effects in cross-domain areas between indoors and outdoors still poses a challenge to high-precision localization. This article proposes a smartphone-based smooth positioning method for indoor–outdoor crossover areas that utilize global navigation satellite system (GNSS), inertial measurement unit (IMU), and ultrawideband (UWB) localization techniques. Essentially, seamless positioning is implemented by optimizing the subfilter factor allocation structure in the two-stage filter structure of the error-state Kalman filter (ESKF) and the federal filter. In addition, a monitoring and optimization algorithm based on empirical mode decomposition (EMD) and gray prediction model is designed to enhance positioning accuracy and system robustness. The experiment results verify that in indoor–outdoor cross-region environments, this method has higher positioning accuracy and robustness than the environmental discrimination-based seamless positioning method. Moreover, the algorithm ensures the smooth transition of positioning effect when the environment changes. This method distinguishes from other solutions by weighting different sensor data based on the data itself rather than environmental discrimination, which can suppress the impact of the ping-pong effect on the results and obtain smoother, more reliable, and higher precision positioning performance.