Robust Dual-Antenna GNSS/INS Attitude Determination via Constrained Ambiguity Resolution and Misalignment Compensation

Attitude information serves as a critical parameter for the safe operation of autonomous platforms such as unmanned ground vehicles (UGVs). To address the challenge of continuous and reliable attitude acquisition in complex environments, this paper proposes a resilient dual-antenna GNSS/INS fusion method. An error-quaternion state model is constructed within a Kalman filter framework, while an observation model integrates dual-antenna GNSS-derived attitude angles and accelerometer outputs. To resolve the critical issue of dynamic baseline ambiguity resolution, a constraint-augmented rapid ambiguity fixing method is developed. Motion-induced line-of-sight acceleration and Earth-referenced centripetal acceleration are compensated to achieve precise accelerometer-based attitude determination in dynamic scenarios. An innovative observation model incorporating angular misalignment estimation is introduced to effectively mitigate systematic errors caused by GNSS/INS axis misalignment. Experiments conducted on two typical ground platforms in GNSS-challenged environments demonstrate that both platforms—a passenger car (T01) with a dual-antenna baseline of 1.115 m and an unmanned vehicle (T02) with a baseline of 0.804 m—achieve pitch and yaw estimation accuracy better than 0.7° (T01: 0.67°/0.54°; T02: 0.49°/0.48°) using dual-antenna GNSS. The GNSS/INS integrated solution further enhances performance, achieving roll/pitch/yaw accuracies of 1.05°/0.32°/0.19° for T01 and 0.38°/0.16°/0.25° for T02, which fully validates the robustness of the proposed algorithm in complex environments.