Self-Constrained Vehicle Inertial Navigation Method With Task-Driven Dynamic Decision Optimization

Abstract

In satellite-denied environments, the inertial navigation system (INS) suffers from error accumulation, whereas integrated solutions often depend on costly external sensors and fail to exploit motion modes effectively. This article reveals the mechanism of navigation enhancement under sparse motion modes, where effective states are intermittent but critical. We propose a task-driven dynamic decision optimization framework for self-constrained vehicle inertial navigation, combining deep learning with geometric state estimation. First, we establish multidimensional self-constraints from vehicle kinematics to suppress error propagation. Next, a Transformer-based network calculates motion mode confidences from inertial data, followed by a Bayesian optimization strategy to dynamically adjust thresholds, prioritizing false alarm suppression under a bounded missed detection tolerance. Furthermore, a state persistence mechanism (SPM) leverages motion continuity to eliminate short-term misjudgments. Finally, motion constraints are fused with inertial data via a manifold-based invariant extended Kalman filter (IEKF), which embeds states on Lie groups to mitigate inconsistency in linearization. The experiments on the Urban Kaist dataset demonstrate that compared with the existing motion mode decision, the average absolute trajectory error of positioning is reduced by more than 8.19%. This verifies the effectiveness of the proposed method, which provides a new solution for high-precision autonomous navigation of vehicles.