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Journal articles on the topic 'Kalman filtering with intermittent observations'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Sinopoli, B., L. Schenato, M. Franceschetti, K. Poolla, M. I. Jordan, and S. S. Sastry. "Kalman Filtering With Intermittent Observations." IEEE Transactions on Automatic Control 49, no. 9 (September 2004): 1453–64. http://dx.doi.org/10.1109/tac.2004.834121.

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2

Plarre, Kurt, and Francesco Bullo. "On Kalman Filtering for Detectable Systems With Intermittent Observations." IEEE Transactions on Automatic Control 54, no. 2 (February 2009): 386–90. http://dx.doi.org/10.1109/tac.2008.2008347.

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3

Chen, Pengpeng, Honglu Ma, Shouwan Gao, and Yan Huang. "Modified Extended Kalman Filtering for Tracking with Insufficient and Intermittent Observations." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/981727.

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This paper is concerned with the Kalman filtering problem for tracking a single target on the fixed-topology wireless sensor networks (WSNs). Both the insufficient anchor coverage and the packet dropouts have been taken into consideration in the filter design. The resulting tracking system is modeled as a multichannel nonlinear system with multiplicative noise. Noting that the channels may be correlated with each other, we use a general matrix to express the multiplicative noise. Then, a modified extended Kalman filtering algorithm is presented based on the obtained model to achieve high tracking accuracy. In particular, we evaluate the effect of various parameters on the tracking performance through simulation studies.

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4

Yilin Mo and Bruno Sinopoli. "Kalman Filtering With Intermittent Observations: Tail Distribution and Critical Value." IEEE Transactions on Automatic Control 57, no. 3 (March 2012): 677–89. http://dx.doi.org/10.1109/tac.2011.2166309.

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5

Battilotti, Stefano, Filippo Cacace, Massimiliano d’Angelo, Alfredo Germani, and Bruno Sinopoli. "Kalman-like filtering with intermittent observations and non-Gaussian noise." IFAC-PapersOnLine 52, no. 20 (2019): 61–66. http://dx.doi.org/10.1016/j.ifacol.2019.12.127.

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6

Zhong, Yigen, and Yonggui Liu. "Flexible optimal Kalman filtering in wireless sensor networks with intermittent observations." Journal of the Franklin Institute 358, no. 9 (June 2021): 5073–88. http://dx.doi.org/10.1016/j.jfranklin.2021.03.025.

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7

Kar, S., B. Sinopoli, and J. M. F. Moura. "Kalman Filtering With Intermittent Observations: Weak Convergence to a Stationary Distribution." IEEE Transactions on Automatic Control 57, no. 2 (February 2012): 405–20. http://dx.doi.org/10.1109/tac.2011.2161834.

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8

Mo, Yilin, and Bruno Sinopoli. "Kalman Filtering with Intermittent Observations: Critical Value for Second Order System." IFAC Proceedings Volumes 44, no. 1 (January 2011): 6592–97. http://dx.doi.org/10.3182/20110828-6-it-1002.03731.

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9

Rohr, Eduardo Rath, Damian Marelli, and Minyue Fu. "Kalman Filtering With Intermittent Observations: On the Boundedness of the Expected Error Covariance." IEEE Transactions on Automatic Control 59, no. 10 (October 2014): 2724–38. http://dx.doi.org/10.1109/tac.2014.2328183.

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10

Censi, Andrea. "Kalman Filtering With Intermittent Observations: Convergence for Semi-Markov Chains and an Intrinsic Performance Measure." IEEE Transactions on Automatic Control 56, no. 2 (February 2011): 376–81. http://dx.doi.org/10.1109/tac.2010.2097350.

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11

Bishop, Adrian N. "Random-Set-Based Estimation in Networked Environments and a Relationship to Kalman Filtering with Intermittent Observations." IFAC Proceedings Volumes 43, no. 19 (2010): 97–102. http://dx.doi.org/10.3182/20100913-2-fr-4014.00010.

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12

Marelli, Damián Edgardo, Tianju Sui, Eduardo Rath Rohr, and Minyue Fu. "Stability of Kalman filtering with a random measurement equation: Application to sensor scheduling with intermittent observations." Automatica 99 (January 2019): 390–402. http://dx.doi.org/10.1016/j.automatica.2018.11.003.

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13

Li, Xin, Yan Wang, and Kourosh Khoshelham. "UWB/PDR Tightly Coupled Navigation with Robust Extended Kalman Filter for NLOS Environments." Mobile Information Systems 2018 (December 5, 2018): 1–14. http://dx.doi.org/10.1155/2018/8019581.

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The fusion of ultra-wideband (UWB) and inertial measurement unit (IMU) is an effective solution to overcome the challenges of UWB in nonline-of-sight (NLOS) conditions and error accumulation of inertial positioning in indoor environments. However, existing systems are based on foot-mounted or body-worn IMUs, which limit the application of the system to specific practical scenarios. In this paper, we propose the fusion of UWB and pedestrian dead reckoning (PDR) using smartphone IMU, which has the potential to provide a universal solution to indoor positioning. The PDR algorithm is based on low-pass filtering of acceleration data and time thresholding to estimate the step length. According to different movement patterns of pedestrians, such as walking and running, several step models are comparatively analyzed to determine the appropriate model and related parameters of the step length. For the PDR direction calculation, the Madgwick algorithm is adopted to improve the calculation accuracy of the heading algorithm. The proposed UWB/PDR fusion algorithm is based on the extended Kalman filter (EKF), in which the Mahalanobis distance from the observation to the prior distribution is used to suppress the influence of abnormal UWB data on the positioning results. Experimental results show that the algorithm is robust to the intermittent noise, continuous noise, signal interruption, and other abnormalities of the UWB data.

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14

Quinn, Courtney, Terence J. O'Kane, and Vassili Kitsios. "Application of a local attractor dimension to reduced space strongly coupled data assimilation for chaotic multiscale systems." Nonlinear Processes in Geophysics 27, no. 1 (February 19, 2020): 51–74. http://dx.doi.org/10.5194/npg-27-51-2020.

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Abstract. The basis and challenge of strongly coupled data assimilation (CDA) is the accurate representation of cross-domain covariances between various coupled subsystems with disparate spatio-temporal scales, where often one or more subsystems are unobserved. In this study, we explore strong CDA using ensemble Kalman filtering methods applied to a conceptual multiscale chaotic model consisting of three coupled Lorenz attractors. We introduce the use of the local attractor dimension (i.e. the Kaplan–Yorke dimension, dimKY) to prescribe the rank of the background covariance matrix which we construct using a variable number of weighted covariant Lyapunov vectors (CLVs). Specifically, we consider the ability to track the nonlinear trajectory of each of the subsystems with different variants of sparse observations, relying only on the cross-domain covariance to determine an accurate analysis for tracking the trajectory of the unobserved subdomain. We find that spanning the global unstable and neutral subspaces is not sufficient at times where the nonlinear dynamics and intermittent linear error growth along a stable direction combine. At such times a subset of the local stable subspace is also needed to be represented in the ensemble. In this regard the local dimKY provides an accurate estimate of the required rank. Additionally, we show that spanning the full space does not improve performance significantly relative to spanning only the subspace determined by the local dimension. Where weak coupling between subsystems leads to covariance collapse in one or more of the unobserved subsystems, we apply a novel modified Kalman gain where the background covariances are scaled by their Frobenius norm. This modified gain increases the magnitude of the innovations and the effective dimension of the unobserved domains relative to the strength of the coupling and timescale separation. We conclude with a discussion on the implications for higher-dimensional systems.

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15

Yang, Chao, Jiangying Zheng, Xiaoqiang Ren, Wen Yang, Hongbo Shi, and Ling Shi. "Multi-Sensor Kalman Filtering With Intermittent Measurements." IEEE Transactions on Automatic Control 63, no. 3 (March 2018): 797–804. http://dx.doi.org/10.1109/tac.2017.2734643.

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16

Arthur, Joseph, Adam Attarian, Franz Hamilton, and Hien Tran. "Nonlinear Kalman filtering for censored observations." Applied Mathematics and Computation 316 (January 2018): 155–66. http://dx.doi.org/10.1016/j.amc.2017.08.002.

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17

Wang, Guoqing, Ning Li, and Yonggang Zhang. "Diffusion nonlinear Kalman filter with intermittent observations." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 15 (July 3, 2017): 2775–83. http://dx.doi.org/10.1177/0954410017716192.

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In this article, we consider the distributed nonlinear state estimation over sensor networks under the diffusion Kalman filter paradigm, where data only exchanges among the neighbourhoods of sensors. We first obtain a novel nonlinear Kalman filter with intermittent observations based on cubature Kalman filter. After that, its equivalent information filter is derived, and the proposed diffusion cubature Kalman filter with intermittent observations is designed based on this information filter. The effectiveness of proposed algorithms is demonstrated by a typical target tracking example, and our algorithm has similar estimation accuracy when comparing with existing algorithms while consuming less computation and communication resources.

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18

Basin, Michael V. "On filtering over Îto-Volterra observations." Journal of Applied Mathematics and Stochastic Analysis 13, no. 4 (January 1, 2000): 347–64. http://dx.doi.org/10.1155/s1048953300000319.

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In this paper, the Kalman-Bucy filter is designed for an Îto-Volterra process over Ito-Volterra observations that cannot be reduced to the case of a differential observation equation. The Kalman-Bucy filter is then designed for an Ito-Volterra process over discontinuous Ito-Volterra observations. Based on the obtained results, the filtering problem over discrete observations with delays is solved. Proofs of the theorems substantiating the filtering algorithms are given.

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19

Karimi, Hazhar Sufi, and Balasubramaniam Natarajan. "Kalman filtered compressive sensing with intermittent observations." Signal Processing 163 (October 2019): 49–58. http://dx.doi.org/10.1016/j.sigpro.2019.05.004.

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20

Li, Wenling, Yingmin Jia, and Junping Du. "Distributed Kalman consensus filter with intermittent observations." Journal of the Franklin Institute 352, no. 9 (September 2015): 3764–81. http://dx.doi.org/10.1016/j.jfranklin.2015.01.002.

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21

De Nicolao, G., and S. Strada. "Kalman filtering with mixed discrete-continuous observations." International Journal of Control 70, no. 1 (January 1998): 71–84. http://dx.doi.org/10.1080/002071798222460.

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22

Das, Subhro, and Jose M. F. Moura. "Distributed Kalman Filtering With Dynamic Observations Consensus." IEEE Transactions on Signal Processing 63, no. 17 (September 2015): 4458–73. http://dx.doi.org/10.1109/tsp.2015.2424205.

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23

He, Chengyang, Chao Tang, and Chengpu Yu. "A Federated Derivative Cubature Kalman Filter for IMU-UWB Indoor Positioning." Sensors 20, no. 12 (June 21, 2020): 3514. http://dx.doi.org/10.3390/s20123514.

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The inertial measurement unit and ultra-wide band signal (IMU-UWB) combined indoor positioning system has a nonlinear state equation and a linear measurement equation. In order to improve the computational efficiency and the localization performance in terms of the estimation accuracy, the federated derivative cubature Kalman filtering (FDCKF) method is proposed by combining the traditional Kalman filtering and the cubature Kalman filtering. By implementing the proposed FDCKF method, the observations of the UWB and the IMU can be effectively fused; particularly, the IMU can be continuously calibrated by UWB so that it does not generate cumulative errors. Finally, the effectiveness of the proposed algorithm is demonstrated through numerical simulations, in which FDCKF was compared with the federated cubature Kalman filter (FCKF) and the federated unscented Kalman filter (FUKF), respectively.

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24

Chen, Hao, Jianan Wang, Chunyan Wang, Jiayuan Shan, and Ming Xin. "Distributed diffusion unscented Kalman filtering based on covariance intersection with intermittent measurements." Automatica 132 (October 2021): 109769. http://dx.doi.org/10.1016/j.automatica.2021.109769.

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25

Ji, S., and X. Yuan. "A GENERIC PROBABILISTIC MODEL AND A HIERARCHICAL SOLUTION FOR SENSOR LOCALIZATION IN NOISY AND RESTRICTED CONDITIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 193–98. http://dx.doi.org/10.5194/isprsarchives-xli-b1-193-2016.

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A generic probabilistic model, under fundamental Bayes’ rule and Markov assumption, is introduced to integrate the process of mobile platform localization with optical sensors. And based on it, three relative independent solutions, bundle adjustment, Kalman filtering and particle filtering are deduced under different and additional restrictions. We want to prove that first, Kalman filtering, may be a better initial-value supplier for bundle adjustment than traditional relative orientation in irregular strips and networks or failed tie-point extraction. Second, in high noisy conditions, particle filtering can act as a bridge for gap binding when a large number of gross errors fail a Kalman filtering or a bundle adjustment. Third, both filtering methods, which help reduce the error propagation and eliminate gross errors, guarantee a global and static bundle adjustment, who requires the strictest initial values and control conditions. The main innovation is about the integrated processing of stochastic errors and gross errors in sensor observations, and the integration of the three most used solutions, bundle adjustment, Kalman filtering and particle filtering into a generic probabilistic localization model. The tests in noisy and restricted situations are designed and examined to prove them.

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26

Ji, S., and X. Yuan. "A GENERIC PROBABILISTIC MODEL AND A HIERARCHICAL SOLUTION FOR SENSOR LOCALIZATION IN NOISY AND RESTRICTED CONDITIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 193–98. http://dx.doi.org/10.5194/isprs-archives-xli-b1-193-2016.

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A generic probabilistic model, under fundamental Bayes’ rule and Markov assumption, is introduced to integrate the process of mobile platform localization with optical sensors. And based on it, three relative independent solutions, bundle adjustment, Kalman filtering and particle filtering are deduced under different and additional restrictions. We want to prove that first, Kalman filtering, may be a better initial-value supplier for bundle adjustment than traditional relative orientation in irregular strips and networks or failed tie-point extraction. Second, in high noisy conditions, particle filtering can act as a bridge for gap binding when a large number of gross errors fail a Kalman filtering or a bundle adjustment. Third, both filtering methods, which help reduce the error propagation and eliminate gross errors, guarantee a global and static bundle adjustment, who requires the strictest initial values and control conditions. The main innovation is about the integrated processing of stochastic errors and gross errors in sensor observations, and the integration of the three most used solutions, bundle adjustment, Kalman filtering and particle filtering into a generic probabilistic localization model. The tests in noisy and restricted situations are designed and examined to prove them.

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27

Kluge, S., K. Reif, and M. Brokate. "Stochastic Stability of the Extended Kalman Filter With Intermittent Observations." IEEE Transactions on Automatic Control 55, no. 2 (February 2010): 514–18. http://dx.doi.org/10.1109/tac.2009.2037467.

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28

Wang, Ning, Yinya Li, Jinliang Cong, and Andong Sheng. "Sequential covariance intersection-based Kalman consensus filter with intermittent observations." IET Signal Processing 14, no. 9 (December 1, 2020): 624–33. http://dx.doi.org/10.1049/iet-spr.2019.0547.

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29

Li, Li, and Yuanqing Xia. "Stochastic stability of the unscented Kalman filter with intermittent observations." Automatica 48, no. 5 (May 2012): 978–81. http://dx.doi.org/10.1016/j.automatica.2012.02.014.

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30

Hoàng, S,, R. Baraille, O. Talagrand, X. Carton, and P. De Mey. "On adaptive filtering for high dimensional systems under parameter uncertainty and its application to satellite data assimilation in oceanography." Journal of Computer Science and Cybernetics 13, no. 2 (March 30, 2016): 18–40. http://dx.doi.org/10.15625/1813-9663/13/2/7987.

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In this paper, the adaptive filtering theory, recently proposed and developed the authors of present work [1-9] for stochastic, encountered in the field of data as simulation in meteorology and oceanography, is reviewed. Several important questions on numerical estimation og the gain matrix, model reduction, structural choices for the gain, filter stability… are discussed. We show the connections of present approach with a standard Kalman filtering. Adaptive filter is implemented along with a Kalman filtering. Adaptive filter is implemented along with a Kalman filter and standard Newton relation method on the four-layer adiabatic Miami Isopycnical Co-ordinate Ocean Model (MICOM) to produce the estimate for the deep oceanic circulation using assimilate synthetic observations of surface height. Numerical results justify high efficiency of the adaptive filter whose performance is slightly better than that of a Kalman filter due to impossibility to correctly specify the error statistics in a Kalman filter.

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31

Morabito, David D., T. Marshall Eubanks, and J. Alan Steppe. "Kalman filtering of Earth orientation changes." Symposium - International Astronomical Union 128 (1988): 257–67. http://dx.doi.org/10.1017/s0074180900119576.

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The orientation of the earth in space changes unpredictably in a rapid and irregular manner, in addition to the uniform rotation of the earth. Observations of extra-terrestrial objects from the surface of the earth are affected by these variations, and knowledge of these changes is required for a variety of geodetic and astrometric purposes as well as being of interest in its own right. The orientation of the earth (specified by a three dimensional rotation vector) is measured by a variety of techniques; combination of these data sets is complicated by irregular changes in the spacing and accuracy of the various time series, and also by the existence of lower dimensional measurements of different linear combinations of the rotation vector components. A Kalman filter has been developed at JPL to smooth and predict earth orientation changes for application to spacecraft navigation by the NASA Deep Space Network. The filter, which provides estimates of the earth orientation changes (and of the excitation of these changes) based on whatever measurements are available, has been used for a number of research applications, both in the reduction of geodetic and astrometric data, and in research into the geophysical causes of earth orientation changes. The JPL Kalman filter uses stochastic models to account statistically for otherwise unpredictable changes in earth orientation; these models make it possible to provide reasonable estimates of the error in the smoothed time series, and to automatically vary the amount of smoothing according to the accuracy and density of the data. The derivation of the stochastic models used by the filter, the implementation of the models into the filter, a statistical description of what the filter does, and the results of filtering specific data sets will be discussed.

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32

Roh, S., M. Jun, I. Szunyogh, and M. G. Genton. "Multivariate localization methods for ensemble Kalman filtering." Nonlinear Processes in Geophysics 22, no. 6 (December 3, 2015): 723–35. http://dx.doi.org/10.5194/npg-22-723-2015.

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Abstract. In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.

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33

Roh, S., M. Jun, I. Szunyogh, and M. G. Genton. "Multivariate localization methods for ensemble Kalman filtering." Nonlinear Processes in Geophysics Discussions 2, no. 3 (May 8, 2015): 833–63. http://dx.doi.org/10.5194/npgd-2-833-2015.

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Abstract. In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (entry-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.

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34

Jensen, Tim Enzlberger. "Spatial resolution of airborne gravity estimates in Kalman filtering." Journal of Geodetic Science 12, no. 1 (January 1, 2022): 185–94. http://dx.doi.org/10.1515/jogs-2022-0143.

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Abstract Airborne gravimetry is an efficient and reliable method to obtain information on the gravity field, fundamental to gravity field modelling, geoid determination, and flood risk mapping. In evaluation and utilization of gravity estimates, two measures are of fundamental importance, namely the accuracy and spatial resolution. These measures are related to one another through the filtering required to suppress observational noise. As strapdown inertial measurement units (IMUs) are increasingly deployed for airborne gravity surveys, the Kalman filter estimation method is routinely used for gravity determination. Since filtering is not applied directly to the observations in Kalman filtering, it is not straightforward to associate the derived gravity estimates with a measure of spatial resolution. This investigation presents a method for deriving spatial resolution by evaluating the transfer function formed after applying a delta function to the observed accelerations. The method is applied to Kalman-filter-derived gravity estimates from an airborne strapdown IMU system, yielding a full-wavelength spatial resolution of 5.5 km at an accuracy of 0.6 mGal. These results are consistent with a comparison with upward continued terrestrial gravity observations.

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35

Bellsky, Thomas, Jesse Berwald, and Lewis Mitchell. "Nonglobal Parameter Estimation Using Local Ensemble Kalman Filtering." Monthly Weather Review 142, no. 6 (May 28, 2014): 2150–64. http://dx.doi.org/10.1175/mwr-d-13-00200.1.

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Abstract The authors study parameter estimation for nonglobal parameters in a low-dimensional chaotic model using the local ensemble transform Kalman filter (LETKF). By modifying existing techniques for using observational data to estimate global parameters, they present a methodology whereby spatially varying parameters can be estimated using observations only within a localized region of space. Taking a low-dimensional nonlinear chaotic conceptual model for atmospheric dynamics as a numerical test bed, the authors show that this parameter estimation methodology accurately estimates parameters that vary in both space and time, as well as parameters representing physics absent from the model.

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36

Zhong, Cheng, Zhonglian Jiang, Xiumin Chu, Tao Guo, and Quan Wen. "Water level forecasting using a hybrid algorithm of artificial neural networks and local Kalman filtering." Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment 233, no. 1 (August 23, 2017): 174–85. http://dx.doi.org/10.1177/1475090217727135.

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The dynamic processes in the tidal reaches of the Yangtze River lead to the complexity of short-term water level forecasting. Historical data of daily water level are obtained for the lower reaches (Anqing–Wuhu–Nanjing) of the Yangtze River. Stationary time series of water level is derived by making the first-order difference with the raw datasets. An artificial neural network–Kalman hybrid model is proposed for water level forecasting, in which the Kalman filtering is introduced for partial data reconstruction. The model is calibrated with the hydrologic daily water level data of years 2014–2016 for MaAnshan station. Comparing with the traditional artificial neural network model, daily water level predictions are improved by the hybrid algorithm. Discrepancies appear under the circumstance of sharp variations of water level observations. Moreover, the implementation strategy of Kalman filtering algorithm is explored, which indicates the superiority of local Kalman filtering.

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37

Yang, Chao, Jianying Zheng, Xiaoqiang Ren, Wen Yang, Hongbo Shi, and Ling Shi. "Corrections to “Multi-Sensor Kalman Filtering With Intermittent Measurements” [Mar 18 797-804]." IEEE Transactions on Automatic Control 63, no. 5 (May 2018): 1545. http://dx.doi.org/10.1109/tac.2018.2816850.

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38

Pelosi, Anna, Hanoi Medina, Joris Van den Bergh, Stéphane Vannitsem, and Giovanni Battista Chirico. "Adaptive Kalman Filtering for Postprocessing Ensemble Numerical Weather Predictions." Monthly Weather Review 145, no. 12 (December 2017): 4837–54. http://dx.doi.org/10.1175/mwr-d-17-0084.1.

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Forecasts from numerical weather prediction models suffer from systematic and nonsystematic errors, which originate from various sources such as subgrid-scale variability affecting large scales. Statistical postprocessing techniques can partly remove such errors. Adaptive MOS techniques based on Kalman filters (here called AMOS), are used to sequentially postprocess the forecasts, by continuously updating the correction parameters as new ground observations become available. These techniques, originally proposed for deterministic forecasts, are valuable when long training datasets do not exist. Here, a new adaptive postprocessing technique for ensemble predictions (called AEMOS) is introduced. The proposed method implements a Kalman filtering approach that fully exploits the information content of the ensemble for updating the parameters of the postprocessing equation. A verification study for the region of Campania in southern Italy is performed. Two years (2014–15) of daily meteorological observations of 10-m wind speed and 2-m temperature from 18 ground-based automatic weather stations are used, comparing them with the corresponding COSMO-LEPS ensemble forecasts. It is shown that the proposed adaptive method outperforms the AMOS method, while it shows comparable results to the member-by-member batch postprocessing approach.

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39

Liang, Yuan, Yinya Li, Sujuan Chen, Guoqing Qi, and Andong Sheng. "Event‐triggered Kalman consensus filter for sensor networks with intermittent observations." International Journal of Adaptive Control and Signal Processing 35, no. 8 (April 22, 2021): 1478–97. http://dx.doi.org/10.1002/acs.3254.

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40

Liu, Xiangdong, Luyu Li, Zhen Li, Tyrone Fernando, and Herbert H. C. Iu. "Stochastic Stability Condition for the Extended Kalman Filter With Intermittent Observations." IEEE Transactions on Circuits and Systems II: Express Briefs 64, no. 3 (March 2017): 334–38. http://dx.doi.org/10.1109/tcsii.2016.2578956.

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41

Yilmaz, M. Tugrul, Timothy DelSole, and Paul R. Houser. "Reducing Water Imbalance in Land Data Assimilation: Ensemble Filtering without Perturbed Observations." Journal of Hydrometeorology 13, no. 1 (February 1, 2012): 413–20. http://dx.doi.org/10.1175/jhm-d-11-010.1.

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Abstract It is well known that the ensemble Kalman filter (EnKF) requires updating each ensemble member with perturbed observations in order to produce the proper analysis-error covariances. While increased accuracy in a mean square sense may be preferable in many applications, less accuracy might be preferable in other applications, especially if the variables being assimilated obey certain conservation laws. In land data assimilation, for instance, the update in soil moisture often produces a water balance residual, in the sense that the input water is not equal to output water. This study shows that suppressing the perturbation of observations in the EnKF and in the weakly constrained ensemble Kalman filter significantly improves the water balance residuals without significantly increasing the state errors.

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42

Chabir, Karim, Taouba Rhouma, Jean Yves Keller, and Dominique Sauter. "State Filtering for Networked Control Systems Subject to Switching Disturbances." International Journal of Applied Mathematics and Computer Science 28, no. 3 (September 1, 2018): 473–82. http://dx.doi.org/10.2478/amcs-2018-0036.

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Abstract State estimation of stochastic discrete-time linear systems subject to unknown inputs has been widely studied, but few works take into account disturbances switching between unknown inputs and constant biases. We show that such disturbances affect a networked control system subject to deception attacks on the control signals transmitted by the controller to the plant via unreliable networks. This paper proposes to estimate the switching disturbance from an augmented state version of the intermittent unknown input Kalman filter. The sufficient stochastic stability conditions of the obtained filter are established when the arrival binary sequence of data losses follows a Bernoulli random process.

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Raboudi, Naila F., Boujemaa Ait-El-Fquih, and Ibrahim Hoteit. "Ensemble Kalman Filtering with One-Step-Ahead Smoothing." Monthly Weather Review 146, no. 2 (February 2018): 561–81. http://dx.doi.org/10.1175/mwr-d-17-0175.1.

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The ensemble Kalman filter (EnKF) is widely used for sequential data assimilation. It operates as a succession of forecast and analysis steps. In realistic large-scale applications, EnKFs are implemented with small ensembles and poorly known model error statistics. This limits their representativeness of the background error covariances and, thus, their performance. This work explores the efficiency of the one-step-ahead (OSA) smoothing formulation of the Bayesian filtering problem to enhance the data assimilation performance of EnKFs. Filtering with OSA smoothing introduces an updated step with future observations, conditioning the ensemble sampling with more information. This should provide an improved background ensemble in the analysis step, which may help to mitigate the suboptimal character of EnKF-based methods. Here, the authors demonstrate the efficiency of a stochastic EnKF with OSA smoothing for state estimation. They then introduce a deterministic-like EnKF-OSA based on the singular evolutive interpolated ensemble Kalman (SEIK) filter. The authors show that the proposed SEIK-OSA outperforms both SEIK, as it efficiently exploits the data twice, and the stochastic EnKF-OSA, as it avoids observational error undersampling. They present extensive assimilation results from numerical experiments conducted with the Lorenz-96 model to demonstrate SEIK-OSA’s capabilities.

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Rakovec, O., A. H. Weerts, J. Sumihar, and R. Uijlenhoet. "Operational aspects of asynchronous filtering for hydrological forecasting." Hydrology and Earth System Sciences Discussions 12, no. 3 (March 20, 2015): 3169–203. http://dx.doi.org/10.5194/hessd-12-3169-2015.

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Abstract. This study investigates the suitability of the Asynchronous Ensemble Kalman Filter (AEnKF) and a partitioned updating scheme for hydrological forecasting. The AEnKF requires forward integration of the model for the analysis and enables assimilation of current and past observations simultaneously at a single analysis step. The results of discharge assimilation into a grid-based hydrological model for the Upper Ourthe catchment in the Belgian Ardennes show that including past predictions and observations in the data assimilation method improves the model forecasts. Additionally, we show that elimination of the strongly non-linear relation between the soil moisture storage and assimilated discharge observations from the model update becomes beneficial for improved operational forecasting, which is evaluated using several validation measures.

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Sharifi, M. A., M. R. Seif, and M. A. Hadi. "A Comparison Between Numerical Differentiation and Kalman Filtering for a Leo Satellite Velocity Determination." Artificial Satellites 48, no. 3 (September 1, 2013): 103–10. http://dx.doi.org/10.2478/arsa-2013-0009.

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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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Yang, Yuanxi, and Weiguang Gao. "Comparison of Adaptive Factors in Kalman Filters on Navigation Results." Journal of Navigation 58, no. 3 (August 19, 2005): 471–78. http://dx.doi.org/10.1017/s0373463305003292.

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The quality of kinematic navigation and positioning depends on the quality of the model describing the vehicle movements and the reliability of the observations. An adaptive Kalman filtering is introduced. Three kinds of adaptive factors based on the discrepancy between the geometrical positions and the kinematic model predictions and a variance component ratio between model predictions and observations are described. A new exponential adaptive factor is established. The theoretical curves of the adaptive factors are drawn and a practical example is given. The errors of four adaptive filtering results and the corresponding curves of the adaptive factors are also drawn. It is shown, by comparison and analysis, that all of the four adaptive factors can control the influences of the vehicle disturbances in movements on the navigation results. The results derived by the adaptive factor constructed by the variance component ratio are slightly better than those derived by other adaptive factors.

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Saynisch, J., and M. Thomas. "Ensemble Kalman-Filtering of Earth rotation observations with a global ocean model." Journal of Geodynamics 62 (December 2012): 24–29. http://dx.doi.org/10.1016/j.jog.2011.10.003.

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Cui, Peng, Hong Guo Zhao, and Mei Zhang. "Optimal State Fusion of Linear Systems with Two Channel Observations." Key Engineering Materials 467-469 (February 2011): 823–28. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.823.

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State fusion problem of linear systems with two channel observations is discussed. A globally optimal recursive algorithm is proposed based on projection formula and innovation analysis. Different linear weighted fusion, the algorithm presented is globally optimal, which is equivalent to centralized Kalman filtering. Moreover, the algorithm is good for real-time demand for innovations from different channels are orthogonal.

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Rakovec, O., A. H. Weerts, J. Sumihar, and R. Uijlenhoet. "Operational aspects of asynchronous filtering for flood forecasting." Hydrology and Earth System Sciences 19, no. 6 (June 23, 2015): 2911–24. http://dx.doi.org/10.5194/hess-19-2911-2015.

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Abstract. This study investigates the suitability of the asynchronous ensemble Kalman filter (AEnKF) and a partitioned updating scheme for hydrological forecasting. The AEnKF requires forward integration of the model for the analysis and enables assimilation of current and past observations simultaneously at a single analysis step. The results of discharge assimilation into a grid-based hydrological model (using a soil moisture error model) for the Upper Ourthe catchment in the Belgian Ardennes show that including past predictions and observations in the data assimilation method improves the model forecasts. Additionally, we show that elimination of the strongly non-linear relation between the soil moisture storage and assimilated discharge observations from the model update becomes beneficial for improved operational forecasting, which is evaluated using several validation measures.

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Rohm, W., K. Zhang, and J. Bosy. "Unconstrained, robust Kalman filtering for GNSS troposphere tomography." Atmospheric Measurement Techniques Discussions 6, no. 5 (October 24, 2013): 9133–62. http://dx.doi.org/10.5194/amtd-6-9133-2013.

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Abstract. The mesoscale variability of water vapour (WV) in the troposphere is a highly complex phenomenon and modeling and monitoring the WV distribution is a very important but challenging task. Any observation technique that can reliably provide WV distribution is essential for both monitoring and predicting weather. GNSS tomography technique is a powerful tool that builds upon the critical ground-based GNSS infrastructure – Continuous Operating Reference Station (CORS) networks and can be used to sense the amount of WV. Previous research suggests that 3-D WV field from GNSS tomography has an uncertainty of 1 hPa. However all the models used in GNSS tomography heavily rely on a priori information and constraints from non-GNSS measurements. In this study, 3-D GNSS tomography models are investigated based on an unconstrained approach with limited a priori information. A case study is designed and the results show that unconstrained solutions are feasible by using a robust Kalman filtering technique and effective removal of linearly dependent observations and parameters. Discrepancies between reference wet refractivity data derived from the Australian Numerical Weather Prediction (NWP) model (i.e. ACCESS) and the GNSS tomography model using both simulated and real data are 4.2 ppm (mm km−1) and 6.5 ppm (mm km−1), respectively, which are essentially in the same order of accuracy. Therefore the accuracy of the integrated values should not be worse than 0.06 m in terms of zenith wet delay and the integrated water vapour is a fifth of this value which is roughly 10 mm.

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