Simultaneous Estimation and Modeling of State-Space Systems Using Multi-Gaussian Belief Fusion

[1]  Biao Huang,et al.  System Identification , 2000, Control Theory for Physicists.

[2]  J. Josiah Steckenrider,et al.  A Probabilistic Model-adaptive Approach for Tracking of Motion with Heightened Uncertainty , 2020 .

[3]  Tomonari Furukawa,et al.  Detection and classification of stochastic features using a multi-Bayesian approach , 2017, 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

[4]  Athina P. Petropulu,et al.  Grid Based Nonlinear Filtering Revisited: Recursive Estimation & Asymptotic Optimality , 2016, IEEE Transactions on Signal Processing.

[5]  James Llinas,et al.  Handbook of Multisensor Data Fusion : Theory and Practice, Second Edition , 2008 .

[6]  Peng Shi,et al.  Fault Estimation and Tolerant Control for Fuzzy Stochastic Systems , 2013, IEEE Transactions on Fuzzy Systems.

[7]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[8]  Carmine Clemente,et al.  Micro-doppler-based in-home aided and unaided walking recognition with multiple radar and sonar systems , 2017 .

[9]  Ilias Bilionis,et al.  Global sensitivity analysis for the design of nonlinear identification experiments , 2019, Nonlinear Dynamics.

[10]  Simo Särkkä,et al.  Bayesian Filtering and Smoothing , 2013, Institute of Mathematical Statistics textbooks.

[11]  Ka-Veng Yuen,et al.  Real‐Time System Identification: An Algorithm for Simultaneous Model Class Selection and Parametric Identification , 2015, Comput. Aided Civ. Infrastructure Eng..

[12]  Azadeh Vosoughi,et al.  On Distributed Linear Estimation With Observation Model Uncertainties , 2017, IEEE Transactions on Signal Processing.

[13]  Ka-Veng Yuen,et al.  Self‐calibrating Bayesian real‐time system identification , 2019, Comput. Aided Civ. Infrastructure Eng..

[14]  Kaare Brandt Petersen,et al.  The Matrix Cookbook , 2006 .

[15]  Carolyn Kalender,et al.  Sparse Grid-Based Nonlinear Filtering , 2013, IEEE Transactions on Aerospace and Electronic Systems.

[16]  Kwangseok Oh,et al.  Inertial Parameter Estimation of an Excavator with Adaptive Updating Rule Using Performance Analysis of Kalman Filter , 2018 .

[17]  Eric Li,et al.  State‐of‐charge estimation of power lithium‐ion batteries based on an embedded micro control unit using a square root cubature Kalman filter at various ambient temperatures , 2019, International Journal of Energy Research.

[18]  Suman Chakravorty,et al.  Particle Gaussian Mixture Filters: Application and Performance Evaluation , 2019, 2019 22th International Conference on Information Fusion (FUSION).

[19]  Guanrong Chen,et al.  Kalman Filtering with Real-time Applications , 1987 .

[20]  H. Sorenson,et al.  Recursive bayesian estimation using gaussian sums , 1971 .

[21]  Jinling Wang,et al.  Evaluating the Performances of Adaptive Kalman Filter Methods in GPS/INS Integration , 2010 .

[22]  Pat Langley,et al.  Induction of Recursive Bayesian Classifiers , 1993, ECML.

[23]  Geir Evensen,et al.  Analysis of iterative ensemble smoothers for solving inverse problems , 2018, Computational Geosciences.

[24]  Tomonari Furukawa,et al.  Bayesian non-field-of-view target estimation incorporating an acoustic sensor , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[25]  Haoyong Yu,et al.  Composite adaptive dynamic surface control using online recorded data , 2016 .

[26]  Emrah Zerdali,et al.  Adaptive Extended Kalman Filter for Speed-Sensorless Control of Induction Motors , 2019, IEEE Transactions on Energy Conversion.

[27]  Garry A. Einicke,et al.  Robust extended Kalman filtering , 1999, IEEE Trans. Signal Process..

[28]  Devendra Potnuru,et al.  Derivative-free square-root cubature Kalman filter for non-linear brushless DC motors , 2016 .

[29]  R. Kohn,et al.  Estimation, Filtering, and Smoothing in State Space Models with Incompletely Specified Initial Conditions , 1985 .

[30]  Vladimir Stojanovic,et al.  Robust Kalman filtering for nonlinear multivariable stochastic systems in the presence of non‐Gaussian noise , 2016 .

[31]  J. Mandel A Brief Tutorial on the Ensemble Kalman Filter , 2009, 0901.3725.

[32]  Chen Zhang,et al.  Probabilistic Anticipation and Control in Autonomous Car Following , 2019, IEEE Transactions on Control Systems Technology.

[33]  Peng Shi,et al.  Sensor fault estimation and tolerant control for Itô stochastic systems with a descriptor sliding mode approach , 2013, Autom..

[34]  Kang-Zhi Liu,et al.  Robust Tracking and Disturbance Rejection for Linear Uncertain System With Unknown State Delay and Disturbance , 2018, IEEE/ASME Transactions on Mechatronics.

[35]  Juraj Kabzan,et al.  Cautious Model Predictive Control Using Gaussian Process Regression , 2017, IEEE Transactions on Control Systems Technology.

[36]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[37]  Pat Langley,et al.  Induction of Selective Bayesian Classifiers , 1994, UAI.

[38]  Glenn Shafer,et al.  Dempster's rule of combination , 2016, Int. J. Approx. Reason..

[39]  Propagation of errors for matrix inversion , 1999, hep-ex/9909031.

[40]  George J. Pappas,et al.  Sensor placement for optimal Kalman filtering: Fundamental limits, submodularity, and algorithms , 2015, 2016 American Control Conference (ACC).

[41]  Edoardo Patelli,et al.  Sensitivity or Bayesian model updating: a comparison of techniques using the DLR AIRMOD test data , 2017 .

[42]  Salim Ibrir Joint state and parameter estimation of non-linearly parameterized discrete-time nonlinear systems , 2018, Autom..

[43]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[44]  Chiuyuan Chen,et al.  Sensor Deployment under Probabilistic Sensing Model , 2018, HPCCT.

[45]  G. A. Einicke,et al.  Smoothing, Filtering and Prediction - Estimating The Past, Present and Future , 2012 .

[46]  J. Janata Introduction to Sensors , 2009 .

[47]  Peter Jan van Leeuwen,et al.  Sequential Monte Carlo with kernel embedded mappings: The mapping particle filter , 2019, J. Comput. Phys..

[48]  H. Sorenson,et al.  Nonlinear Bayesian estimation using Gaussian sum approximations , 1972 .

[49]  Juan M. Corchado,et al.  A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking , 2017, Sensors.

[51]  Yang Yang,et al.  Schmidt-Kalman Filter with Polynomial Chaos Expansion for State Estimation , 2019, 2019 22th International Conference on Information Fusion (FUSION).

[52]  Xuemei Wang,et al.  Robust centralized and weighted measurement fusion Kalman estimators for uncertain multisensor systems with linearly correlated white noises , 2017, Inf. Fusion.

[53]  Hyochoong Bang,et al.  Utilizing Out-of-Sequence Measurement for Ambiguous Update in Particle Filtering , 2018, IEEE Transactions on Aerospace and Electronic Systems.

[54]  Luigi Chisci,et al.  An unscented Kalman filter based navigation algorithm for autonomous underwater vehicles , 2016 .

[55]  Edwin Lughofer,et al.  Reliable All-Pairs Evolving Fuzzy Classifiers , 2013, IEEE Transactions on Fuzzy Systems.

[56]  Xuemei Wang,et al.  Robust centralized and weighted measurement fusion Kalman estimators for multisensor systems with multiplicative and uncertain-covariance linearly correlated white noises , 2017, J. Frankl. Inst..

[57]  Chia-Nan Ko,et al.  Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter , 2013 .

[58]  A. Hammouch,et al.  A new approach of classification for non-Gaussian distribution upon competitive training , 2012, 2012 IEEE International Conference on Complex Systems (ICCS).

[59]  Vladimir Stojanovic,et al.  Joint state and parameter robust estimation of stochastic nonlinear systems , 2016 .

[60]  Lennart Ljung,et al.  Kernel methods in system identification, machine learning and function estimation: A survey , 2014, Autom..

[61]  Luca Delle Monache,et al.  Comparison of numerical weather prediction based deterministic and probabilistic wind resource assessment methods , 2015 .

[62]  T. Subba Rao,et al.  Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB , 2004 .

[63]  Nicolas Boizot,et al.  A Real-Time Adaptive High-Gain EKF, Applied to a Quadcopter Inertial Navigation System , 2014, IEEE Transactions on Industrial Electronics.

[64]  Jinde Cao,et al.  Composite Learning Adaptive Dynamic Surface Control of Fractional-Order Nonlinear Systems , 2020, IEEE Transactions on Cybernetics.

[65]  Juan M. Corchado,et al.  Partial Consensus and Conservative Fusion of Gaussian Mixtures for Distributed PHD Fusion , 2017, IEEE Transactions on Aerospace and Electronic Systems.

[66]  A. Willsky,et al.  On the fixed-interval smoothing problem † , 1981 .

[67]  Jonathan R. Stroud,et al.  Understanding the Ensemble Kalman Filter , 2016 .

[68]  Thomas A. Mazzuchi,et al.  Comparison of a grid-based filter to a Kalman filter for the state estimation of a maneuvering target , 2011, Optical Engineering + Applications.

[69]  D. L. Alspach,et al.  Gaussian Sum Approximations in Nonlinear Filtering and Control , 1974, Inf. Sci..

[70]  Tomonari Furukawa,et al.  Multi-dimensional belief fusion of multi-Gaussian structures , 2020, Inf. Fusion.

[71]  Qian Fan,et al.  Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network , 2014 .

[72]  Henk A. P. Blom,et al.  Aircraft Mass and Thrust Estimation Using Recursive Bayesian Method , 2018 .

[73]  A. Sharma,et al.  A Cubature Kalman Filter Based Power System Dynamic State Estimator , 2017, IEEE Transactions on Instrumentation and Measurement.

[74]  David E. Culler,et al.  Calibration as parameter estimation in sensor networks , 2002, WSNA '02.

[75]  John E. Mottershead,et al.  The sensitivity method in finite element model updating: A tutorial (vol 25, pg 2275, 2010) , 2011 .

[76]  Christopher K. Wikle,et al.  A Bayesian Adaptive Ensemble Kalman Filter for Sequential State and Parameter Estimation , 2016, 1611.03835.

[77]  Vinay Kumar,et al.  Tunable Multilevel Probabilistic Sensing Model based Intrusion Detection in Gaussian Distributed WSNs , 2017, 2017 14th IEEE India Council International Conference (INDICON).

[78]  Daniel Gatica-Perez,et al.  A probabilistic kernel method for human mobility prediction with smartphones , 2015, Pervasive Mob. Comput..

[79]  Niclas Bergman,et al.  Recursive Bayesian Estimation : Navigation and Tracking Applications , 1999 .

[80]  Audun Jøsang Cumulative and Averaging Fission of Beliefs , 2010, Inf. Fusion.

[81]  Junping Du,et al.  Robust unscented Kalman filter with adaptation of process and measurement noise covariances , 2016, Digit. Signal Process..

[82]  Thomas Moore,et al.  A Generalized Extended Kalman Filter Implementation for the Robot Operating System , 2014, IAS.

[83]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[84]  Andrew W. Smyth,et al.  A marginalized unscented Kalman filter for efficient parameter estimation with applications to finite element models , 2018, Computer Methods in Applied Mechanics and Engineering.

[85]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[86]  D. Söffker,et al.  Proportional-Integral-Observer: A brief survey with special attention to the actual methods using ACC Benchmark , 2015 .

[87]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[88]  Alexander Katriniok,et al.  Adaptive EKF-Based Vehicle State Estimation With Online Assessment of Local Observability , 2016, IEEE Transactions on Control Systems Technology.

[89]  Rongrong Wang,et al.  Actuator and sensor faults estimation based on proportional integral observer for TS fuzzy model , 2017, J. Frankl. Inst..

[90]  Avraham Adler,et al.  Lambert-W Function , 2015 .