A Method of Airborne Gravimetry by Combining Strapdown Inertial and New Satellite Observations via Dynamic Networks

Abstract

We revisit the concept of scalar gravity anomaly determination by an airborne strapdown INS–GNSS system. We built on the previously investigated concepts (mainly within 1995–2005 period) while trying to decrease the error spectrum of the system caused by accelerometer biases at lower frequencies and GNSS-position/velocity noise at shorter wavelengths. We propose to determine the random long-term accelerometer bias through combination of −GRACE + GOCE data that provide an unbiased field with 80 km resolution while the decrease in velocity noise is expected by precise-point-positioning (PPP) method that merges satellite-phase observations from GPS and Galileo. In the absence of Galileo constellation we focus our practical demonstration on the gravity-anomaly determination via INS/GNSS data filtering. We present first the modeling of an extended Kalman filter/smoother that determines the gravity anomaly together with the trajectory, which is a preferred method over the cascade determination (i.e. separate estimation of trajectory and specific forces, GNSS acceleration and low-pass filtering of the merged signal). Second, we show how to incorporate the same modeling within the concept of dynamic networks. This approach allows imposing cross-over conditions on the state of gravity anomaly at trajectory intersections while estimating the sensor and trajectory errors at the same time. This is indeed rigorous formulation of the problem that is expected to surpass the conditioning via cross-over adjustment that in previous investigations followed the filtering-smoothing. Despite the remaining challenges of the method of dynamic network caused by large number of parameters (i.e. > 10⁶), we present first assessment of such implementation that was obtained within European FP7 GAL project.