Abstract
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 Eötvös, which is very close to the level of Gaussian noise (0.011 Eötvös) 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.
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Acknowledgments
This work is financially aided by a grant from Canada’s Natural Sciences and Engineering Research Council (NSERC) to the second author. The GOCE Level 2b data were provided by Prof. I.N. Tziavos of the Aristotle University of Thessaloniki within the GOCESeaComb project. The two anonymous reviewers are thanked for their comments and suggestions for the improvement of this paper.
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Piretzidis, D., Sideris, M.G. (2017). Application of the Recursive Least-Squares Adaptive Filter on Simulated Satellite Gravity Gradiometry Data. In: Vergos, G., Pail, R., Barzaghi, R. (eds) International Symposium on Gravity, Geoid and Height Systems 2016. International Association of Geodesy Symposia, vol 148. Springer, Cham. https://doi.org/10.1007/1345_2017_24
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DOI: https://doi.org/10.1007/1345_2017_24
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