Gravity Field Parameter Estimation Using QR Factorization

This study compares the accuracy of the estimated geopotential coefficients when QR factorization is used instead of the classical method applied at our institute, namely the generation of normal equations that are solved by means of Cholesky decomposition. The objective is to evaluate the gain in numerical precision, which is obtained at considerable extra cost in terms of computer resources. Therefore, a significant increase in precision must be realized in order to justify the additional cost. Numerical simulations were done in order to examine the performance of both solution methods. Reference gravity gradients were simulated, using the EIGEN-GL04C gravity field model to degree and order 300, every 3 seconds along a near-circular, polar orbit at 250 km altitude. The simulation spanned a total of 60 days. A polar orbit was selected in this simulation in order to avoid the 'polar gap' problem, which causes inaccurate estimation of the low-order spherical harmonic coefficients. Regularization is required in that case (e.g., the GOCE mission), which is not the subject of the present study. The simulated gravity gradients, to which white noise was added, were then processed with the GINS software package, applying EIGEN-CG03 as the background gravity field model, followed either by the usual normal equation computation or using the QR approach for incremental linear least squares. The accuracy assessment of the gravity field recovery consists in computing the median error degree-variance spectra, accumulated geoid errors, geoid errors due to individual coefficients, and geoid errors calculated on a global grid. The performance, in terms of memory usage, required disk space, and CPU time, of the QR versus the normal equation approach is also evaluated.