Two unbiased converted measurement Kalman filtering algorithms with range rate

Abstract This paper develops extended Kalman filtering algorithms for a generic Gough-Stewart platform assuming realistically available sensors such as length sensors, rate gyroscopes, and accelerometers. The basic idea is to extend existing methods for satellite attitude estimation. The nondeterministic methods are meant to be a practical alternative to existing iterative, deterministic methods for real-time estimation of platform configuration.

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Recovery of Rapid Water Mass Changes (RWMC) by Kalman Filtering of GRACE Observations

Remote Sensing ◽

10.3390/rs12081299 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1299

Author(s):

Guillaume Ramillien ◽

Lucía Seoane ◽

Maike Schumacher ◽

Ehsan Forootan ◽

Frédéric Frappart ◽

...

Keyword(s):

Kalman Filtering ◽

Water Mass ◽

Extreme Weather Events ◽

Grace Data ◽

Weather Events ◽

Continental Hydrology ◽

Realistic Description ◽

Range Rate ◽

Recovery Approach ◽

Along Track

We demonstrate a new approach to recover water mass changes from GRACE satellite data at a daily temporal resolution. Such a product can be beneficial in monitoring extreme weather events that last a few days and are missing by conventional monthly GRACE data. The determination of the distribution of these water mass sources over networks of juxtaposed triangular tiles was made using Kalman Filtering (KF) of daily GRACE geopotential difference observations that were reduced for isolating the continental hydrology contribution of the measured gravity field. Geopotential differences were obtained from the along-track K-Band Range Rate (KBRR) measurements according to the method of energy integral. The recovery approach was validated by inverting synthetic GRACE geopotential differences simulated using GLDAS/WGHM global hydrology model outputs. Series of daily regional and global KF solutions were estimated from real GRACE KBRR data for the period 2003–2012. They provide a realistic description of hydrological fluxes at monthly time scales, which are consistent with classical spherical harmonics and mascons solutions provided by the GRACE official centers but also give an intra-month/daily continuity of these variations.

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A Comparison Between Numerical Differentiation and Kalman Filtering for a Leo Satellite Velocity Determination

Artificial Satellites ◽

10.2478/arsa-2013-0009 ◽

2013 ◽

Vol 48 (3) ◽

pp. 103-110 ◽

Cited By ~ 3

Author(s):

M.A. Sharifi ◽

M.R. Seif ◽

M.A. Hadi

Keyword(s):

Kalman Filtering ◽

Velocity Vector ◽

Numerical Differentiation ◽

Filter Method ◽

Newton Interpolation ◽

Satellite Velocity ◽

Kalman Filter Method ◽

Range Rate ◽

Noise Amplification ◽

Leo Satellite

Abstract The kinematic orbit is a time series of position vectors generally obtained from GPS observations. Velocity vector is required for satellite gravimetry application. It cannot directly be observed and should be numerically determined from position vectors. Numerical differentiation is usually employed for a satellite’s velocity, and acceleration determination. However, noise amplification is the single obstacle to the numerical differentiation. As an alternative, velocity vector is considered as a part of the state vector and is determined using the Kalman filter method. In this study, velocity vector is computed using the numerical differentiation (e.g., 9-point Newton interpolation scheme) and Kalman filtering for the GRACE twin satellites. The numerical results show that Kalman filtering yields more accurate results than numerical differentiation when they are compared with the intersatellite range-rate measurements.

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