In strapdown inertial navigation system and global navigation satellite system-based airborne gravimetry, there is a circular problem between the navigation solution and the gravity vector estimation. On one hand, the gravity vector estimation depends on the navigation solution. On the other hand, the navigation solution requires a predetermined gravity model. The normal gravity, which differs from the actual gravity by an amount known as the gravity disturbance, is commonly used for the navigation solution, and this will result in errors of gravimetry. We reviewed this problem and found that an iterative method can be effective if there were no gyroscope biases in the inertial navigation system measurements. Iteratively, the differences between the estimated gravity vector and actual gravity vector can converge to constant values in this specific condition. If we know the gravity values at the endpoints of each survey line, these constant values can be compensated for by matching these endpoints. After such compensations, the repeatability of the estimated horizontal gravity components can be improved significantly. An application to real airborne data was performed to test the validity of the new method. The results yielded an internal accuracy in the horizontal component of approximately 3 mGal with a spatial resolution of 4.8 km.

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Gravity surveys are an important research topic in geophysics and geodynamics. This paper investigates a method for high accuracy large scale gravity anomaly data reconstruction. Based on the airborne gravimetry technology, a flight test was carried out in China with the strap-down airborne gravimeter (SGA-WZ) developed by the Laboratory of Inertial Technology of the National University of Defense Technology. Taking into account the sparsity of airborne gravimetry by the discrete Fourier transform (DFT), this paper proposes a method for gravity anomaly data reconstruction using the theory of compressed sensing (CS). The gravity anomaly data reconstruction is an ill-posed inverse problem, which can be transformed into a sparse optimization problem. This paper uses the zero-norm as the objective function and presents a greedy algorithm called Orthogonal Matching Pursuit (OMP) to solve the corresponding minimization problem. The test results have revealed that the compressed sampling rate is approximately 14%, the standard deviation of the reconstruction error by OMP is 0.03 mGal and the signal-to-noise ratio (SNR) is 56.48 dB. In contrast, the standard deviation of the reconstruction error by the existing nearest-interpolation method (NIPM) is 0.15 mGal and the SNR is 42.29 dB. These results have shown that the OMP algorithm can reconstruct the gravity anomaly data with higher accuracy and fewer measurements.