A method for UAV airborne gravimetry based on integrated PPP-RTK/SINS navigation and butterworth filtering

Unmanned aerial vehicle (UAV) airborne gravimetry offers high operational flexibility at relatively low cost, making it an important means of rapidly acquiring regional gravity field information. However, the achievable accuracy of UAV airborne gravimetry remains limited by both the accuracy of the navigation solution and the design of the signal processing scheme. This paper proposes a UAV airborne gravimetry method integrating PPP-RTK/SINS navigation with Butterworth filtering. High-precision PPP-RTK solutions serve as external measurements for Kalman-filter integration with SINS. Gravity disturbances are derived from the specific-force equation, with low-frequency components extracted using a Butterworth low-pass filter. Estimation accuracy is evaluated against the XGM 2019e gravity model. Experimental results indicate that, compared with the conventional scheme combining PPP/SINS navigation and FIR filtering, the proposed scheme combining PPP-RTK/SINS navigation with Butterworth filtering reduces the gravity disturbance RMSE along the survey line from 7.47 mGal to 3.01 mGal, while achieving a spatial resolution of approximately 2.08 km. The fourth-order recursive Butterworth structure replaces a 2,500th-order FIR filter, thereby substantially reducing computational complexity. Theoretical analysis and simulation results demonstrate that the proposed method is well suited to UAV airborne gravimetry and provides a useful reference for engineering applications and algorithm validation.