Refinement of UAV barometer altimetry model and GNSS/SINS integrated positioning enhancement

doi:10.11947/j.AGCS.2025.20250276

Abstract

Abstract:

In addition to the GNSS/SINS module, UAV navigation systems are often equipped with low-cost micro-electro-mechanical systems (MEMS) barometers. However, existing barometric height measurements suffer from inconsistency with GNSS references and are susceptible to disturbances such as airflow and wind, resulting in significant random errors that limit their reliable application in UAV navigation. To address this issue, this paper proposes a novel enhanced method for integrated GNSS/SINS navigation of UAVs based on refined error modeling of barometric height measurements. Specifically, on the basis of the classical 15-state GNSS/SINS model, a barometer bias state is introduced and parameterized using a random walk model. Furthermore, considering the high volatility of barometric errors, a significant correlation analysis is conducted on the random errors of barometric height measurements in rotary-wing UAVs. A neural network model based on motion state is proposed to predict barometric height errors, establishing a mapping relationship between inertial measurement unit (IMU) outputs and barometric random errors, which serves as the foundation for optimizing the stochastic model in barometer-enhanced positioning. Experimental results from UAV flight tests in mountainous areas demonstrate that the proposed refined barometric error modeling method, when integrated with GNSS/SINS using extended Kalman filter (EKF), significantly improves vertical positioning accuracy compared to conventional methods. In open areas with high-dynamic maneuvers, the vertical positioning accuracy is improved by 15.20%; in canyon environments with signal occlusion, it is improved by 37.74%; and under 10-second GNSS-denied conditions, the accuracy is improved by 44.20%. These results confirm that the proposed method offers strong robustness and higher positioning accuracy in complex scenarios such as mountainous regions.

Key words: GNSS/SINS, UAV, barometer, convolutional neural network, altitude constraint

CLC Number:

P228

Hanyun SONG, Xin LI, Guanwen HUANG, Hang LI. Refinement of UAV barometer altimetry model and GNSS/SINS integrated positioning enhancement[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(11): 1980-1991.

Figures/Tables 12

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参数	数值
陀螺零偏不稳定性/（（°）/h）	10
陀螺随机游走/（（°）/）	0.7
加速度计稳定性/mg	0.1
加速度计随机游走/（mg/）	0.5
气压计压力分辨率/mbar	0.02
气压计测高标称精度/m	0.2

场景	轴向	RMSE/m			U向提升/（%）
场景	轴向	GNSS/SINS	GNSS/SINS/BARO（固定R_h）	GNSS/SINS/BARO（BHE-Net）	U向提升/（%）
开阔机动	E	0.547 7	0.510 9	0.464 4	15.20
	N	0.595 3	0.590 9	0.507 5
	U	0.523 9	0.459 1	0.410 9
	3D	0.963 7	0.906 1	0.801 3
峡谷遮挡	E	0.754 0	0.702 6	0.678 4	37.74
	N	1.287 8	1.150 0	0.838 1
	U	0.283 8	0.182 3	0.176 7
	3D	1.519 1	1.360 0	1.092 6
GNSS拒止（10 s）	E	1.474 6	0.845 4	0.780 9	44.20
	N	3.524 1	1.766 2	1.417 0
	U	0.674 2	0.379 3	0.376 8
	3D	3.879 8	1.994 5	1.661 1