BiLSTM-Driven Adaptive GNSS/INS/ODO Integration With Dynamic NHC/ZUPT Noise Tuning for Vehicle Navigation

Abstract

To address the performance degradation of vehicle-borne integrated navigation caused by frequent Global Navigation Satellite System (GNSS) signal attenuation in complex urban environments, and to overcome the limitations of conventional nonholonomic constraint (NHC) and zero-velocity update (ZUPT), which rely on static empirical noise parameters, fail to adapt to time-varying vehicle dynamics, and are prone to inducing filter divergence, this article proposes a GNSS real-time kinematic (RTK)/Inertial Navigation System (INS)/odometry (ODO) integrated navigation algorithm that fuses bidirectional long–short-term memory (BiLSTM)-based motion state recognition with adaptive NHC/ZUPT noise tuning. First, a lightweight temporal classifier based on BiLSTM is constructed. By extracting temporal dependencies from raw multisensor observations, the classifier achieves real-time, high-accuracy recognition of vehicle stationary, straight-line, and turning states, overcoming the misclassification problem of conventional threshold-based methods in state transition regions. Second, a dynamic noise tuning mechanism driven by state probabilities is designed: under stationary conditions, a polynomial decay strategy is employed to tighten the ZUPT noise covariance, thereby suppressing the divergence of inertial sensor biases; under turning conditions, an exponential expansion strategy is adopted to dynamically inflate the NHC noise covariance, relaxing the lateral velocity constraint that would otherwise violate the Ackermann steering assumption. Finally, the adaptive measurement noise matrix is incorporated into a Kalman filtering framework for multisource information fusion. Real-world vehicle tests demonstrate that, compared with conventional fixed-parameter filtering methods, the proposed method effectively suppresses filter oscillations caused by state misclassification, enhances positioning robustness and resilience under both stationary and turning conditions, and ultimately improves positioning accuracy.