TY  - JOUR
A1  - Baerenzung, Julien
A1  - Holschneider, Matthias
A1  - Wicht, Johannes
A1  - Lesur, Vincent
A1  - Sanchez, Sabrina
T1  - The Kalmag model as a candidate for IGRF-13
JF  - Earth, planets and space
N2  - We present a new model of the geomagnetic field spanning the last 20 years and called Kalmag. Deriving from the assimilation of CHAMP and Swarm vector field measurements, it separates the different contributions to the observable field through parameterized prior covariance matrices. To make the inverse problem numerically feasible, it has been sequentialized in time through the combination of a Kalman filter and a smoothing algorithm. The model provides reliable estimates of past, present and future mean fields and associated uncertainties. The version presented here is an update of our IGRF candidates; the amount of assimilated data has been doubled and the considered time window has been extended from [2000.5, 2019.74] to [2000.5, 2020.33].
KW  - Geomagnetic field
KW  - Secular variation
KW  - Assimilation
KW  - Kalman filter
KW  - Machine learning
Y1  - 2020
U6  - https://doi.org/10.1186/s40623-020-01295-y
SN  - 1880-5981
VL  - 72
IS  - 1
PB  - Springer
CY  - New York
ER  - 
TY  - JOUR
A1  - Ropp, Guillaume
A1  - Lesur, Vincent
A1  - Bärenzung, Julien
A1  - Holschneider, Matthias
T1  - Sequential modelling of the Earth’s core magnetic field
JF  - Earth, Planets and Space
N2  - We describe a new, original approach to the modelling of the Earth's magnetic field. The overall objective of this study is to reliably render fast variations of the core field and its secular variation. This method combines a sequential modelling approach, a Kalman filter, and a correlation-based modelling step. Sources that most significantly contribute to the field measured at the surface of the Earth are modelled. Their separation is based on strong prior information on their spatial and temporal behaviours. We obtain a time series of model distributions which display behaviours similar to those of recent models based on more classic approaches, particularly at large temporal and spatial scales. Interesting new features and periodicities are visible in our models at smaller time and spatial scales. An important aspect of our method is to yield reliable error bars for all model parameters. These errors, however, are only as reliable as the description of the different sources and the prior information used are realistic. Finally, we used a slightly different version of our method to produce candidate models for the thirteenth edition of the International Geomagnetic Reference Field.
KW  - geomagnetic field
KW  - secular variation
KW  - Kalman filter
KW  - IGRF
Y1  - 2020
U6  - https://doi.org/10.1186/s40623-020-01230-1
SN  - 1880-5981
VL  - 72
IS  - 1
PB  - Springer
CY  - New York
ER  - 
TY  - JOUR
A1  - Tu, Rui
A1  - Wang, Rongjiang
A1  - Walter, Thomas R.
A1  - Diao, FaQi
T1  - Adaptive recognition and correction of baseline shifts from collocated GPS and accelerometer using two phases Kalman filter
JF  - Advances in space research
N2  - The real-time recognition and precise correction of baseline shifts in strong-motion records is a critical issue for GPS and accelerometer combined processing. This paper proposes a method to adaptively recognize and correct baseline shifts in strong-motion records by utilizing GPS measurements using two phases Kalman filter. By defining four kinds of learning statistics and criteria, the time series of estimated baseline shifts can be divided into four time intervals: initialization, static, transient and permanent. During the time interval in which the transient baseline shift is recognized, the dynamic noise of the Kalman filter system and the length of the baseline shifts estimation window are adaptively adjusted to yield a robust integration solution. The validations from an experimental and real datasets show that acceleration baseline shifts can be precisely recognized and corrected, thus, the combined system adaptively adjusted the estimation strategy to get a more robust solution. (C) 2014 COSPAR. Published by Elsevier Ltd. All rights reserved.
KW  - GPS
KW  - Strong-motion
KW  - Baseline shift
KW  - Kalman filter
KW  - Integration
Y1  - 2014
U6  - https://doi.org/10.1016/j.asr.2014.07.008
SN  - 0273-1177
SN  - 1879-1948
VL  - 54
IS  - 9
SP  - 1924
EP  - 1932
PB  - Elsevier
CY  - Oxford
ER  -