New Strapdown Airborne Gravimetry Algorithms: Testing with Real Flight Data

The paper presents new algorithms for strapdown airborne gravimetry and the results of processing real data from a survey flight carried out by DTU Space, Denmark, in 2019 using an iMAR system. The developed algorithms cover all stages of airborne data postprocessing, namely: computing GNSS solutions, initial alignment of gravime-ter's inertial measurement unit (IMU), IMU-GNSS integration, and gravity estimation. At the latter, we solve two different problems, one of which is esti-mation of only the vertical component of the gravity vector (scalar gravimetry) and the other is estimation of all three gravity vector components (vector gravimetry). The scalar gravimetry algorithm is based on modeling gravity in time and Kalman filtering. The accuracy of the gravity estimates obtained from processing the iMAR flight data is 1.1 mGal (RMS) based on the cross-over analysis (for the filter cutoff frequency of 1/100 Hz). The vector gravimetry algorithm is based on spatial gravity modeling and using the Kalman filter in the information form. The accuracy of the gravity horizontal component estimates was evaluated by comparison with EGM2008 and equals 2 mGal (STD), which can be regarded as a promising result for airborne vector gravimetry.