Optimizing the Integration of Inertial Navigation Systems and Air Data for Enhanced Vertical Channel Stability in Autonomous Ground Vehicles

The vertical channel of strap-down inertial navigation systems (SINS) in autonomous ground vehicles plays a critical role in maintaining stability and accuracy, particularly in challenging environments. This paper proposes an integrated approach that combines air data system outputs and inertial measurement unit (IMU) outputs in a damping loop to enhance vertical channel stability. To optimize the efficiency of the vertical channel damping loop, a genetic algorithm (GA) is employed to fine-tune specific parameters, ensuring a robust and adaptive response under varying conditions. In this approach, when a global navigation satellite system (GNSS) signal is available, the method's parameters are tuned based on optimizing a performance index that includes altitude data from the vertical channel mechanization and the GNSS. The optimized parameters are then used during GNSS signal blockages to maintain the system's performance. The proposed method is first designed and then practically implemented on a ground autonomous vehicle platform. The results demonstrate significant improvements in stability compared to conventional approaches. By integrating air data with IMU outputs, the approach not only enhances vertical channel accuracy but also reduces reliance on external sources such as GNSS, making the system more reliable for real-world applications.