Application of the Recursive Least-Squares Adaptive Filter on Simulated Satellite Gravity Gradiometry Data

摘要

This study investigates the applicability of the recursive least-squares (RLS) adaptive filter for gravity field modelling applications. Simulated satellite gravity gradients are used to assess the performance of the algorithm. The synthetic data follow the behavior of the Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) mission observations. An analysis is carried out, where the convergence speed, computational efficiency and optimal impulse response of the adaptive filter are examined. The behavior of the filtered gravity gradients in the time and spectral domain is also studied. The algorithm is capable of converging to a mean-square error (MSE) of 0.013 Eotvos, which is very close to the level of Gaussian noise (0.011 Eotvos) added to the synthetic observations. Although the RLS algorithm shows a fast convergence speed, a strong disadvantage that should be considered before its implementation is its reduced time efficiency.