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The analysis errors being below the observation error, our algorithm produces solutions able to fit the MF and SV data within the error bars provided by COV-OBS.x1. Figure 7 SV (bottom, units: (log _{10}), in nT/year) and MF (top, unit: (log _{10}), in nT) spectra at Earth surface (2014.5).
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The ultimate boundedness of both the observation error and the speed tracking error is proven.
The asymptotic vanishing of both the observation error and the speed tracking error is proven.
(e o the observation errors, H the forward operator, see below).
Figure 8 Dynamics of the observation errors.
Different types of the observation errors, and like missing of some data, registration errors, synchronization errors, are also accumulated.
Figure 7 Effects of the channel observation error on throughput.
The resulting observation errors contain, ideally, the thermal noise and dynamic stress errors.
Below we briefly define the rationale behind the probability models used for each of the components of our model, including observation models (e.g. observation error in growth), process models (e.g. growth), and the priors for parameters.
Each receiving station was calibrated daily and the observed error was generally below 2°.
The presence of observation noise will naturally affect the adaptive filter performance, but we will rely on the general insight that the target cancelation error of LMS-type adaptive filters is theoretically several dB below the observation noise level [30, 44].
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