Deep Learning-Aided Adaptive Weighting for Tightly Coupled GNSS/INS Integration in Challenging Environments

The performance of integrated navigation using global navigation satellite systems (GNSS) and inertial navigation systems (INS) in challenging urban environments is highly dependent on the quality of GNSS observations, particularly under multipath and non-line-of-sight conditions. Conventional weighting strategies based on predefined empirical relationships with satellite elevation angle or signal quality are limited in characterizing the nonlinear and time-varying characteristics of GNSS measurement errors. To address this issue, this study proposes a deep learning-based adaptive weighting method for tightly coupled GNSS/INS integration. Satellite elevation angle, signal-to-noise ratio (SNR), observation residuals, and normalized observation residuals are jointly utilized to characterize observation quality, and a neural network is employed to estimate satellite-specific measurement noise parameters for constructing the measurement noise covariance matrix. Since ground-truth measurement noise is difficult to obtain directly, the network is optimized using position, velocity, and attitude errors as the training objective. Vehicle-based experiments were conducted in representative environments, including open-sky areas, tree-lined roads, urban street canyons, and overpass sections. The proposed method achieves east, north, and up position root mean square errors (RMSEs) of 0.38, 0.75, and 1.14 m along route A, corresponding to reductions of 15.6%, 17.6%, and 17.4% compared with the combined elevation angle and SNR weighting strategy. Along route B, the corresponding RMSEs are 0.56, 0.71, and 1.18 m, representing reductions of 25.3%, 43.2%, and 34.1%, respectively. Consistent improvements are also achieved in velocity and attitude estimation. These results demonstrate that the proposed adaptive weighting strategy effectively improves the accuracy and stability of tightly coupled GNSS/INS integration under challenging observation conditions.