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24 results for “Kalman filter”
Extended Kalman filters for close-range navigation to noncooperative targets
<p>The data sets provided here are associated to the paper “Extended Kalman filters for close-range navigation to noncooperative targets” available at this <a name="_Hlk36545040"></a><a href="https://doi.org/10.1016/j.asr.2023.10.038"><span>link</span></a>. These allow recreating the simulations discussed in Sections 5.2 – for the results plotted in Figure 7 – 5.3 (Figures 9-10), and 5.4 (Figures 11-12).</p> <p>That paper presents a set of dynamic filters for estimating the relative roto-translational state and the main parameters of a noncooperative target from an observing chaser satellite during close proximity operations. The proposed different options address a wide range of design possibilities for the architecture of the relative navigation system. All filters are derived from a common, general, core shaped as a dynamic multiplicative extended Kalman filter using dual quaternions. This allows exploiting the advantages of handling the pose (i.e., attitude and position) in a multiplicative fashion, while improving the accuracy in the estimation of the angular and linear relative velocities, as well as enabling the estimation of some meaningful parameters of the target spacecraft (e.g., the ratios of the moments of inertia, position and orientation of the principal axes frame). Moreover, by adopting relative kinematics and dynamics equations in dual quaternions, the inherent coupling of the six degrees-of-freedom motion is addressed with no approximations.</p> <p>All filters take as observations only the noisy pose measurements from an electro-optical device. For each proposed formulation, numerical simulations are carried out to show the behaviour of the filter within a scenario representative of close-range target inspection at conclusion of the mid-range rendezvous.</p>
Performance Criteria and Example Parameter Sets Comparing Different Variants of the Ensemble Kalman Filter as Applied to Volcanology
<p>This dataset contains the results of various Ensemble Kalman Filter (EnKF) inversions in which synthetic GNSS and InSAR observations from an inflating magma system are assimilated into numerical models of rock deformation around a pressurized ellipsoidal magma reservoir. Each inversion uses a different variant of the EnKF, with changes to workflow meta-parameters such as the number of ensemble members or the particular update algorithm used. In particular, each filter variant is evaluated by comparing the final output model to the original synthetic model. The specific performance criteria used include (1) the root mean square error (RMSE) between the model predictions and the assimilated observations, as well as normalized misfit terms measuring the filter's ability to resolve (2) reservoir wall tensile stress, (3) easily-observable unique parameters such as reservoir position and aspect ratio, and (4) difficult-to-derive non-unique parameters such as the specific size and internal pressure of the reservoir. The assimilated data include two different scenarios, one in which inflation is caused by pressurization and another in which it is driven by a lateral reservoir expansion. Both datasets are tested with each EnKF variant. Finally, we include example matrices from within an EnKF update step to demonstrate inter-parameter correlations that develop during the assimilation and how they can be mitigated through randomization.</p>
Results of "Ensemble Kalman Filter for the Thermosphere Ionosphere", CHAMP neutral density assimilation into CTIPe for March 20, 2007
<p>These data are the result of assimilating neutral density measurements from the CHAMP satellite on March 20, 2007 into the CTIPe model and and comparison of results with observations made by the GRACE satellite. Data assimilation is performed in three configurations: Configuration (i) is ds, state correction. Configuration (ii) is dfds, both input estimatation and state correction. Configuration (iii) is df, estimation of model inputs only.</p> <p>This data is associated with the following publication:</p> <blockquote> <p>Codrescu S., M.V. Codrescu, and M. Fedrizzi (2018), An Ensemble Kalman Filter for the Thermosphere-Ionosphere, Space Weather, 16, doi:<a href="http://dx.doi.org/10.1002/2017SW001752" title="Link to external resource: 10.1002/2017SW001752">10.1002/2017SW001752</a>.</p> </blockquote> <p> </p>
RRR/RAPID input and output files corresponding to "Underlying Fundamentals of Kalman Filtering for River Network Modeling"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RRR/RAPID input and output files that were used in the study reported in:</p> <ul> <li> <p>Emery, C. M., C. H. David, K. M. Andreadis, M. J. Turmon, J. T. Reager, and J. M. Hobbs (2020), Underlying Fundamentals of Kalman Filtering for River Network Modeling, Journal of Hydrometeorology, 21, 453-474, DOI: 10.1175/JHM-D-19-0084.1.</p> </li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. </p> <p><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>In the final version of the published manuscript, Figure 5a, Figure 5b, Figure 5c, and Figure SF1 are inaccurate. The issue in these figures is that they were all prepared with an incorrect indexing relating observed and simulated discharge, hence observations at any one location were consistently being compared to simulations at another different location. As a result, all values of "measured" discharge errors (i.e. Bias, STDE, and RMSE) are incorrect. This issue did not affect the values of "estimated" errors, nor did it affect all values of the Nash-Sutcliffe effeciency that are presented. The figures published in the manuscript can all be recreated using the files in which "BUG_DO_NOT_USE" was appended to the name. Correct figures can also be created using corresponding file names that were not so appended. </p> <p>Note that corrected versions of Figure 5a, Figure 5b, Figure 5c, and Figure SF1 all retain the same strong linear relationships that are discussed in the paper. The slope of the daily discharge STDE trend initially reported as <span class="math-tex">\(\alpha = 0.3876\)</span> in Figure 5c changes to <span class="math-tex">\(\alpha = 0.4507\)</span> after correction. The resulting value of the ideal inflation factor hence changes from <span class="math-tex">\(I = {1 \over 0.3876} \approx 2.58\)</span> to <span class="math-tex">\(I = {1 \over 0.4507} \approx 2.22\)</span>. This updated ideal inflation factor has no impact on the conclusions reached in the manuscript because it remains closer to <span class="math-tex">\(I = 2.58\)</span> than to <span class="math-tex">\(I = 1\)</span> or <span class="math-tex">\(I = 5\)</span>, <em>i.e.</em> the three values that were evaluated.</p> <p>Additionally, a faulty version 1.3.1 of the Python toolbox netCDF4 led to incorrect interpretation of _FillValue in which every data point of value greater than _FillValue was interpreted as masked. This created discrepancies in the following three files, which were updated between V1 and V2 of this dataset: "timeseries_rap_exp01.csv", "timeseries_rap_exp18.csv", and "stats_rap_exp18.csv". Faulty versions of the same files have "BUG_NETCDF4" appended to their names. Correct files have been recreated with file names that were not so appended. </p>
Station catalog in "Real-time earthquake location based on the Kalman filter formulation"
<p>Station catalog used to locate earthquakes in Parkfield, California, in the manuscript entitled "Real-time earthquake location based on the Kalman filter formulation" submitted to Geophysical Research Letters</p>
A Kalman Filter Approach to the Fusion of Acceleration, GNSS position and Rotation Sensor Data from Robot Motions
<p><strong>GNSS data:</strong></p> <ul> <li>Instrument: Javad antenna and Septentrio receiver</li> <li>sampling rate: 100 Hz</li> <li>Bandwidth of loop filter: auto adjust</li> <li>Relative positioning </li> <li>Baseline: ultra short with distance of 5 m</li> <li>files in Rinex format: Rover (moving antenna) and Base (stationary antenna), .20G (GLONASS Navigation data), .20N (GPS Navigation data), .20L (Galileo Navigation data), .20O (Observations)</li> </ul> <p><strong>Accelerometer data:</strong></p> <ul> <li>Instrument: EpiSensor and Centaur Digitizer</li> <li>Sampling rate: 250 Hz</li> <li>Unit: counts</li> <li>unfiltered</li> <li>file: XKUK_centaur-6_1233_20200908_114500.seed</li> </ul> <p><strong>Angular rate data:</strong></p> <ul> <li>Instrument: IMU KvH 1750 (includes accelerometer and rotational sensor)</li> <li>Sampling rate: 250 Hz</li> <li>Unit gyro: rad/s</li> <li>Unit accelerometer: g (gravitational acceleration)</li> <li>file: LOGGING_1750_IMU_1308K004_11_57_25_250.csv</li> </ul> <p><strong>Robot Feedback:</strong></p> <ul> <li>Instrument: KUKA model AGILUS KR 6 R900 sixx</li> <li>Sampling rate: 250 Hz</li> <li>Unit translation: m</li> <li>Unit rotation: degree</li> <li>files: kuka_motion_*.txt, 1-4 are consecutive in time.</li> </ul> <p><strong>Experiments:</strong></p> <ul> <li>T: translations, R: rotations, XL, L, S denote the relative amplitudes</li> <li>10 experiments: TLRXL, TLRXL, TLRL, TLRL, TLRS, unfinished TLRS, TLRS, TLRS, TSRS, TSRS (Robot feedback (1,2), angular rate, GNSS data)</li> <li>9 experiments: TLRXL, TLRXL, TLRL, TLRL, TLRS, unfinished TLRS, TLRS, TSRS, TSRS (Robot feedback (3,4), accelerometer data</li> </ul>
Kalman filter-based integration of GNSS and InSAR observations for local non-linear strong deformations
<p>The published database is related to the calculations presented in the paper "Kalman filter-based integration of GNSS and InSAR observations for local non-linear strong deformations". The main catalogue contains three folders named as GNSS, Campaign, and DInSAR.</p> <p>In the GNSS folder, the time series of XYZ coordinates and uncertainties estimated in the post-processing scenario for RES1, PI02, PI03, PI04, PI05, and PI16 permanent stations are provided. The GNSS calculations were performed at the Wrocław University of Environmental and Life Sciences in the ITRF2014 reference frame.</p> <p>The Campaign folder contains the results of epoch-based GNSS measurements and was used in the article as a verification data source. The Campaign results, prepared by the Military University of Technology, were used in the quality analyses. In order to co-locate the permanent PI02, PI04, PI05, and PI16 receivers with the nearest campaign points, a cross-reference was performed. The epoch-based time series of XYZ coordinates and uncertainties are provided in the ITRF2014 reference frame.</p> <p>The DInSAR interferograms were prepared at the Wrocław University of Environmental and Life Sciences and the results were stored in two directories named as Ascending and Descending. To perform a point-based unification of DInSAR and GNSS techniques, it was necessary to acquire the data from pixels intersected by the GNSS permanent station's locations. The DInSAR time series contain displacements (DSP), incidence angles (INC_ANG), heading angles (HEAD_ANG), and coherence (COH) data.</p>
Wind farm power short-term prediction using WRF model and Kalman filtering
<p>This repository contains the data used and generated in the paper:</p> <p>Mamani, R., & Hendrick, P. (2019). Wind farm power short-term prediction using WRF model and Kalman filtering. ECOS 2019</p>
Perturbed Synthetic SWOT Datasets for Testing and Development of a Kalman Filter Approach to Estimate Daily Discharge
<p><strong>1. Introduction</strong></p> <p>Datasets are used to evaluate the performance of a Kalman filter approach to estimate daily discharge. This is a perturbed version of synthetic SWOT datasets consisting of 15 river sections, which are commonly agreed datasets for evaluating the performance of SWOT discharge algorithms (Frasson et al., 2020, 2021). The benchmarking manuscript entitled “A Kalman Filter Approach for Estimating Daily Discharge Using Space-based Discharge Estimates” is currently under review at Water Resources Research. Once the manuscript is accepted, its DOI will be included here.</p> <p> </p> <p><strong>2. </strong><strong>File description</strong></p> <p>The datasets are generally divided into two categories: river information (River_Info) and time series data (Timeseries_Data). River information provides fundamental and general river characteristics, whereas time series data offers daily reach-averaged data for each reach. In time series data, the data mainly contains three components: true data, perturbed measurements, and true and perturbed flow law parameters (A0, an, and b). For each reach, there are 10000 realizations of perturbed measurements per time step and there are 100 realizations of time-invariant perturbed flow law parameters through a Monte Carlo simulation (Frasson et al., 2023). Moreover, to support our proposed Kalman filter approach to estimate daily discharge, the datasets provide the median of the perturbed discharge, river width, water surface slope, and change in the cross-sectional area, as well as the uncertainty of the perturbed discharge and change in the cross-sectional area based on the interquartile range (Fox, 2015).</p> <p>To support reproducibility and facilitate example usage, we now include a MATLAB code package (<code>KalmanFilter_Code.zip</code>) that demonstrates how to run the Kalman filter approach using the Missouri Downstream case as an example. </p> <p>Datasets are contained in a .mat file per river. The detailed groups and variables are in the following:</p> <p><strong>River_Info</strong></p> <p>Name: River name, data type: char</p> <p>QWBM: Mean annual discharge from the water balance model WBMsed (Cohen et al., 2014)</p> <p>rch_bnd: Reach boundaries measured in meters from the upstream end of the model</p> <p>gdrch: Good reaches in the study. They were used to exclude small reaches defined around low-head dams and other obstacles where Manning’s equation should not be applied.</p> <p><strong>Timeseries_Data</strong></p> <p>t: Time measured in days since the first day or “0-January-0000” for cases when specific dates were available. Dimension: 1, time step.</p> <p>A: Reach-averaged cross-sectional area of flow in m<sup>2</sup>. Dimension: Reach, time step.</p> <p>Q_true: True reach-averaged discharge (m<sup>3</sup>/s). Dimension: Reach, time step.</p> <p>Q_ptb: Perturbed discharge (m<sup>3</sup>/s), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>med_Q_ptb: Median perturbed discharge (m<sup>3</sup>/s) across the 10000 realizations. Dimension: Good reach, time step.</p> <p>sigma_Q_ptb: Uncertainty of the perturbed discharge (m<sup>3</sup>/s), calculated based on the interquartile range. Dimension: Good reach, time step.</p> <p>W_true: True reach-averaged river width (m). Dimension: Reach, time step.</p> <p>W_ptb: Perturbed river width (m), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>med_W_ptb: Median perturbed river width (m) across the 10000 realizations. Dimension: Good reach, time step.</p> <p>H_true: True reach-averaged water surface elevation (m). Dimension: Reach, time step.</p> <p>H_ptb: Perturbed water surface elevation (m), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>S_true: True reach-averaged water surface slope (m/m). Dimension: Reach, time step.</p> <p>S_ptb: Perturbed water surface slope (m/m), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>med_S_ptb: Median perturbed water surface slope (m/m) across the 10000 realizations. Dimension: Good reach, time step.</p> <p>dA_true: True reach-averaged change in the cross-sectional area (m<sup>2</sup>). Dimension: Good reach, time step.</p> <p>dA_ptb: Perturbed change in the cross-sectional area (m<sup>2</sup>), including 10000 realizations for each measurement. Dimension: Good reach, time step, 10000.</p> <p>med_dA_ptb: Median perturbed change in the cross-sectional area (m<sup>2</sup>) across the 10000 realizations. Dimension: Good reach, time step.</p> <p>sigma_dA_ptb: Uncertainty of the perturbed change in the cross-sectional area (m<sup>2</sup>), calculated based on the interquartile range. Dimension: Good reach, time step.</p> <p>A0_true: True baseline cross-sectional area (m<sup>2</sup>). Dimension: Good reach, 1.</p> <p>A0: Perturbed baseline cross-sectional area (m<sup>2</sup>), including 100 realizations for each parameter. Dimension: Good reach, 100.</p> <p>na_true: True friction coefficient. Dimension: Good reach, 1.</p> <p>na: Perturbed friction coefficient, including 100 realizations for each parameter. Dimension: Good reach, 100.</p> <p>b_true: True exponent coefficient. Dimension: Good reach, 1.</p> <p>b: Perturbed exponent coefficient, including 100 realizations for each parameter. Dimension: Good reach, 100.</p>
Implementation of an adaptive bias-aware extended Kalman filter for sea-ice data assimilation in the HARMONIE-AROME numerical weather prediction system: numerical experiments
<p>This data set provides the post-processed output of the numerical experiments performed to assess the possible effects of applying sea ice data assimilation within the surface analysis procedure of the HARMONIE-AROME NWP system. Results of five numerical experiments are provided:</p> <ul> <li>HA-REF – reference experiment <em>without</em> sea ice data assimilation applied, and with blending for upper-air initialization</li> <li>HA-EKF – sensitivity experiment with sea ice data assimilation applied, and with blending for upper-air initialization</li> <li>3DVAR-REF – reference experiment <em>without</em> sea ice data assimilation applied, and with 3DVAR for the upper-air analysis</li> <li>3DVAR-EKF – sensitivity experiment with sea ice data assimilation applied, and with 3DVAR for the upper-air analysis</li> <li>3DVAR-EKF-TS – sensitivity experiment with sea ice data assimilation, and with 3DVAR for the upper-air analysis using coupled surface and atmospheric data assimilation procedures</li> </ul> <p>For the HA-REF and HA-EKF experiments a subset of the gridded model output is provided; for the 3DVAR-REF, 3DVAR-EKF and 3DVAR-EKF-TS experiments a subset of the gridded model output and model data extracted at the positions of the SYNOP and TEMP stations within the model domain are provided. Additionally, in-situ observations, covering the same time period as the 3DVAR-REF, 3DVAR-EKF, 3DVAR-EKF-TS experiments, are provided.</p>
Royer & Pastres, Kalman Filter applied to Oxygen estimation in Land-based Aquaculture
<p>These two csv files contains the data relative to the paper "<strong>Data assimilation as a key step towards the implementation of an efficient management of dissolved oxygen in land-based aquaculture</strong>" submitted to Aquaculture International.</p>
Seismic datasets in "Real-time earthquake location based on the Kalman filter formulation"
<p>Synthetic seismic dataset and Parkfield seismic dataset that used in the manuscript entitled "Real-time earthquake location based on the Kalman filter formulation" submitted to Geophysical Research Letters</p>
MaskUKF: An Instance Segmentation Aided Unscented Kalman Filter for 6D Object Pose and Velocity Tracking - Accompanying Data
<p>Dataset and evaluation data associated to the publication "MaskUKF: An Instance Segmentation Aided Unscented Kalman Filter for 6D Object Pose and Velocity Tracking".</p>
1D simulation of land subsidence with ensemble Kalman filter
<p>This folder contains:</p> <p>a) output files from 1D simulations of land subsidence with ensemble Kalman filter in heterogeneous and highly compressible aquitards, reported in Zapata-Norberto et al., 2024.</p> <p>b) R Scripts to reproduce all figures and supplementary material in the cited reference.</p>
Data accompanying the article "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020"
<p>The .zip file contains temporal-spatial averaged metrics for evaluating simulations against observed ice thickness, concentration, volume, and drift. These quantities are presented in the manuscript "Arctic sea ice data assimilation combining an ensemble Kalman filter with a novel Lagrangian sea ice model for the winter 2019–2020"</p> <p>Subfolders are named by the experiment IDs, including metrics obtained from the relevant experimental results and observations.</p> <p>In case information is missing, do not hesitate to contact chengsukun@hotmail.com</p> <p>We thank Pavel Sakov for helpful discussions and improvement regarding the EnKF-C code and Jiping Xie for contributing the TOPAZ interface to sea ice observations. We are grateful for the support from Timothy Williams and Anton Korosov regarding the environments of neXtSIM and its analysis tools. The work is funded by the DASIM-II grant from ONR (grant nos. N00014-18-1-2493 and N00014-18-1-2204). Alberto Carrassi, Christopher K. R. T. Jones, Ali Aydo ̆gdu, and Pierre Rampal acknowledge the support of the project SASIP funded by Schmidt Futures – a philanthropic initiative that seeks to improve societal outcomes through the development of emerging science and technologies. Sukun Cheng and Laurent Bertino were co-funded by the FOCUS project from the Research Council of Norway (grant no. 301450), and Alberto Carrassi and Yumeng Chen are also supported by the UK National Centre for Earth Observation (grant no. NCEO02004). Computations were carried out on the Norwegian Supercomputing InfrastructureSigma2 (grants nn2993k for computing and NS2993K for data storage)</p>
Daily cross-correlation functions for "Time-lapse monitoring of seismic velocity associated with 2011 Shinmoe-dake eruption using seismic interferometry: an extended Kalman filter approach"
<p>The daily cross-correlation functions used in Nishida et al. 2020. We used three-component seismograms recorded at eight stations (six broadband sensors and two short-period sensors with a natural frequency of 1 Hz) from May 1st, 2010 to April 30th, 2018. Five stations were deployed by the Earthquake Research Institute, the University of Tokyo, and the other three were deployed by the National Research Institute for Earth Science and Disaster Prevention (NIED). The data can be found in the HDF5 file. You can also find a python code of an implementation of an extended Kalman filter/smoother for time-lapse monitoring of seismic velocity at GitHub (https://github.com/qnishida/eKlfS). The code estimates the temporal change in seismic velocities using this data set. </p>
Fault detection in Robotic Swarm Aggregation using a Kalman Filter - Appendix
<p>The appendix for my bachelor thesis: Fault detection in Robotic Swarm Aggregation using a<br> Kalman Filter. In the images of the aggregation experiments the different lines refer to the number of clustering robots.</p>
Data from: Taking advantage of hybrid bioinspired intelligent algorithm with decoupled extended Kalman filter for optimizing growing and pruning radial basis function network
Open the record for dataset details and reuse information.
Implementation of an Ensemble Kalman Filter in the Community Multiscale Air Quality Model (CMAQ Model v5.1) for Data Assimilation of Ground-level PM2.5: Model Simulation Outputs
<p>This data sets are model outputs from CMAQ simulations. The output contains only PM2.5 variable after combining related aerosol species. File format is netCDF binary. File naming convention for Domain 1 (D1) is D1_EXP_DATE_TIME_e000.nc where EXP is the control experiment (CTR) or the assimilation experiments (DA_icbc), DATE means YYYYMMDD format date, TIME indicates 2 digits UTC time, and e000 represents ensemble mean result. Also, file naming convention for Domain 2 (D2) is D2_EXP_CASE_DATE_TIME_e000 where EXP is the control experiment (CTR) or the assimilation experiments (DA_ic and DA_icbc), CASE is the simulation cases for ANL or PRD, DATE means YYYYMMDD format date, TIME indicates 2 digits UTC time, and e000 represents ensemble mean result. For the processed and assimilated observation data in this study for D1 and D2, the file names are D1_OBS_DATA_YYYYMMDDhh.txt and D2_OBS_DATA_YYYYMMDDhh.txt, respectively, where YYYYMMDD is date format and hh is UTC.</p>
Supplementary Material for manuscript 'A Novel Adaptive Factor-based H∞ Cubature Kalman Filter for Autonomous Underwater Vehicle'
<p>composed of simulation codes and experiment datas.</p>
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