An Advanced Formulation of Kalman Filter Time Series Reference Frame Realization for Geophysical Applications

Author(s):  
Xiaoping Wu ◽  
Bruce Haines ◽  
Michael Heflin ◽  
Felix Landerer

<p>A Kalman filter and time series approach to the International Terrestrial Reference Frame (ITRF) realization (KALREF) has been developed and used in JPL. KALREF combines weekly or daily SLR, VLBI, GNSS and DORIS data and realizes a terrestrial reference frame in the form of time-variable geocentric station coordinate time series. The origin is defined at nearly instantaneous Center-of-Mass of the Earth system (CM) sensed by weekly SLR data and the scale is implicitly defined by the weighted averages of those of weekly SLR and daily VLBI data. The standard KALREF formulation describes the state vector in terms of time variable station coordinates and other constant parameters. Such a formulation is fine for station positions and their uncertainties or covariance matrices at individual epochs. However, coordinate errors are strongly correlated over time given KALREF’s unique nature of combining different technique data with various frame strengths through local tie measurements and co-motion constraints and its use of random walk processes. For long time series and large space geodetic networks in the ITRF, KALREF cannot keep track of such correlations over time. If they are ignored when forming geocentric displacements for geophysical inverse or network shift geocenter motion studies, the covariance matrices of coordinate differences cannot adequately represent those of displacements. Consequently, significant non-uniqueness and inaccuracies would occur in the results of studies using such matrices. To overcome this difficulty, an advanced KALREF formulation is implemented that features explicit displacement parameters in the state vector that would allow the Kalman filter and smoother to compute and return covariance matrices of displacements. The use of displacement covariance matrices reduces the impact of time correlated errors and completely solves the non-uniqueness problem. However, errors in the displacements are still correlated in time. Further calibrations are needed to accurately assess covariance matrices of derivative quantities such as averages, velocities and accelerations during various time periods. We will present KALREF results of the new formulation and their use along with newly reprocessed RL06 GRACE gravity data in a new unified inversion for geocenter motion.</p>

2015 ◽  
Vol 120 (5) ◽  
pp. 3775-3802 ◽  
Author(s):  
Xiaoping Wu ◽  
Claudio Abbondanza ◽  
Zuheir Altamimi ◽  
T. Mike Chin ◽  
Xavier Collilieux ◽  
...  

2020 ◽  
Author(s):  
Chunmei Zhao ◽  
Lingna Qiao ◽  
Tianming Ma

<p>The development of satellite space geodesy technology makes the establishment of global terrestrial reference frame based on the Earth’s center of mass become reality. Precise and stable terrestrial reference frame is the foundation of the Earth science research, while determination and analysis of the position of the Earth's center of mass and its change is an important part to build high precision terrestrial reference frame. Based on GNSS weekly solutions provided by IGS, the geocenter motion (GM) time series between 2007 and 2017 are obtained by means of net translation method. Then the amplitude of the annual term of geocentric motion is 2.27mm, 1.84mm and 2.13mm in the direction of X, Y and Z respectively, and the amplitude of the half-year term is 0.1mm, 0.20mm and 0.15mm respectively. In addition, some other inter-annual changes with relatively small contribution rate are found. Finally, in order to get reliable GM prediction ,two kinds of methods are used, which are ARMA and SSA+ARMA. In the short-term prediction, the accuracy of the two methods is the same, both can reach the millimeter level of prediction accuracy, but SSA+ARMA is more stable. SSA+ARMA algorithm is much better in the medium and long-term scale, and it can provide 1mm medium term prediction accuracy and 1.5mm long term prediction accuracy.</p>


2020 ◽  
Author(s):  
Hongjuan Yu ◽  
Krzysztof Sośnica ◽  
Yunzhong Shen

<p>Accurate quantification and analysis of geocenter motion are of great significance to the construction and maintenance of the international terrestrial reference frame and its geodetic and geophysical applications. Here, the time series of 13-year geocenter motion coordinates (from 2006 to 2019) is determined by using the network shift approach from Satellite Laser Ranging (SLR) observations to Lageos1 / 2. Then, the geocenter motion time series is analyzed by using singular spectrum analysis. The principal components of geocenter motion are determined with the w-correlation criterion and two principal components with large w-correlation are regarded as the periodic signals. The results show that the annual periodic terms are clearly detectable in all out of three coordinate components, whereas the semi-annual term is only detected in the X-component. Moreover, weak periodic oscillations of 3 to 4 months exist in the X- and Y-components. Besides weak periodic signals with periods of about 8 months and 1 month for the X- and Y-components, respectively, a significant periodic signal of about 2.8 years exists in the  Z-component. Compared to the geocenter motion signals derived by the Center for Space Research (CSR) and Wrocław University of Environmental and Life Sciences (WUELS), both amplitude and phase agree well, with a better consistency with those from CSR, especially for the X- and Y-components.</p>


Author(s):  
Mohammad H. Elahinia ◽  
Hashem Ashrafiuon ◽  
Mehdi Ahmadian ◽  
Daniel J. Inman

This paper presents a robust nonlinear control that uses a state variable estimator for control of a single degree of freedom rotary manipulator actuated by Shape Memory Alloy (SMA) wire. A model for SMA actuated manipulator is presented. The model includes nonlinear dynamics of the manipulator, a constitutive model of the Shape Memory Alloy, and the electrical and heat transfer behavior of SMA wire. The current experimental setup allows for the measurement of only one state variable which is the angular position of the arm. Due to measurement difficulties, the other three state variables, arm angular velocity and SMA wire stress and temperature, cannot be directly measured. A model-based state estimator that works with noisy measurements is presented based on the Extended Kalman Filter (EKF). This estimator predicts the state vector at each time step and corrects its prediction based on the angular position measurements. The estimator is then used in a nonlinear and robust control algorithm based on Variable Structure Control (VSC). The VSC algorithm is a control gain switching technique based on the arm angular position (and velocity) feedback and EKF estimated SMA wire stress and temperature. The state vector estimates help reduce or avoid the undesirable and inefficient overshoot problem in SMA one-way actuation control.


2019 ◽  
Vol 69 (4) ◽  
pp. 33-44
Author(s):  
Grosinger Patrik ◽  
Šolek Peter

AbstractThis paper presents a simple-to-use system for estimating non-measurable components of crane state vector considering parameter changes. To obtain them, it is possible to use a numerical derivative, where the measurement noise causes great inaccuracies, or the Luenberger observer and Kalman filter, which require knowledge of the dynamics of the controlled system, which is constantly changing with the crane.


2018 ◽  
Author(s):  
Luis Gustavo C. Uzai ◽  
André Y. Kashiwabara

Time series are sequence of values distributed over time. Analyzing time series is important in many areas including medical, financial, aerospace, commercial and entertainment. Change Point Detection is the problem of identifying changes in meaning or distribution of data in a time series. This article presents Spec, a new algorithm that uses the graph spectrum to detect change points. The Spec was evaluated using the UCR Archive which is a large da- tabase of different time series. Spec performance was compared to the PELT, ECP, EDM, and gSeg algorithms. The results showed that Spec achieved a better accuracy compared to the state of the art in some specific scenarios and as efficient as in most cases evaluated.


2021 ◽  
Vol 31 (3) ◽  
pp. 425-435
Author(s):  
Edimilson Lima de Assis ◽  
Mauro José de Deus Morais ◽  
Jorge De Oliveira Eichemberg ◽  
Valéria Rigamonte Azevedo de Assis ◽  
Hugo Macedo Junior ◽  
...  

Introduction: coronavirus is part of a group of RNA viruses belonging to the Coronaviridae family, widely distributed in humans and other mammals. Currently, it has been seriously affecting the whole world, without a definitive cure yet. Objective: to analyse the association between the HDI and confirmed cumulative cases of COVID-19 that occurred during epidemiological week 16 to 53 of 2020, in the State of Acre. Methods: this is an ecological study of descriptive time series, evaluating the State of Acre and its 22 municipalities affected by COVID-19, in the period corresponding to the epidemiological weeks 16 to 53 of 2020. The State of Acre and its municipalities are aggregated by five regions with a total of approximately 881 thousand inhabitants, with an HDI of 0.663. Rio Branco is the state capital with 407,000 inhabitants. The 22 municipalities were analyzed, relating the HDI variables, confirmed cases per day and number of inhabitants to each other. Results: it was observed that the population evaluated, affected by COVID-19 during SE 16 to 53 of 2020, in the State of Acre, had as predominant general characteristics brown skin color, male sex, and the evolution to death from the disease was related with older age and comorbidity. Acre had a mortality rate (deaths per 100,000 inhabitants) of 90.9 and a lethality rate of 1.9%, with the highest mortality rate observed in the municipality of Rio Branco (121.3/100,000 inhabitants) and lethality in Rodrigues Alves (2.9%). The incidence of COVID-19 in Acre was 4,759.9 cases per 100,000 inhabitants, the municipalities of Assis Brasil and Xapuri had the highest incidences in the state with 10273.7 and 9330.8 new cases per 100,000 inhabitants, respectively. Conclusion: although the accumulated numbers of cases are different for the same day, the behavior is very similar, that is, the curves vary in the same way over time, regardless of the municipality observed.


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