A new approach to linear filtering and prediction problems" transaction of the asme~journal of basic

By R. Kálmán. 1960

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Citations in the corpus

Time-series forecasting with deep learning: a survey (2020, methodology): “tering As examined in [39], Bayesian ﬁlters [45] and RNNs are both similar in their maintenance of a hidden state which is recursively updated over time. For Bayesian ﬁlters, such as the Kalman ﬁlter [46], inference is performed by updating the sufﬁcient statistics of the latent state – using a seri”
Lifelong Generative Modeling (2017, background): “The idea of learning in a continual manner has been explored extensively in machine learning, seeded by the seminal works of lifelong-learning [126, 125, 117], online-learning [32, 9, 11, 12] and sequential linear gaussian models [105, 36] such as the Kalman Filter [56] and its non-linear counterpar”
Kalman Filtering and Neural Networks (2001, background): “One such function that has been found to be particularly appropriate for pattern classification problems, and for which a sound statistical basis exists, is a cost function based on minimizing crossentropy [23].”
Balance and Ensemble Kalman Filter Localization Techniques (2011, unknown): “The ensemble Kalman ﬁlter (EnKF; Evensen 1994) is a Monte Carlo approximation to the traditional ﬁlter of Kalman (1960) that is suitable for high-dimensional problems such as numerical weather prediction (NWP).”
Applied Stochastic Differential Equations (2019, unknown): “For linear Gaussian models, we can compute these likelihoods exactly with Kalman filters, whereas for nonlinear and non-Gaussian models we need to approximate them. Fortunately, the same approximations that can be used for approximate continuous-discrete filtering also provide approximations to the ”
Kalman filters for non-linear systems: a comparison of performance (2004, methodology): “This mean and covariance are updated analytically with the Kalman filter (KF) algorithm (Kalman 1960, Sorenson 1985).”
Gaussian filters for nonlinear filtering problems (2000, background): “The problem has been the subject of considerable research interest during the past several decades because it has many significant applications in science and engineering such as navigational and guidance systems, radar tracking, sonar ranging, and satellite and airplane orbit determination [11], [1”
A Survey on Recent Advances in Vehicular Network Security, Trust, and Privacy (2019, background): “Kalman filter [77].”
On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems (2007, background): “As in the continuous-time case, the closed form solutions to these equations or to the equations for the moments (e.g., mean and covariance) of the distributions can only be found in a few special cases [ 30 ]–[32] and approximations are generally needed.”
Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging (2007, methodology): “The prototype of the SDA methods, the Kalman filter (KF) [ Kalman, 1960 ] was developed in the 1960s for optimal control of systems governed by linear equations.”
Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches (1995, background): “1 The Discrete Kalman Filter This section describes the filter in its original formulation (Kalman 1960) where the measurements occur and the state is estimated at discrete points in time.”
Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation (2016, methodology): “Continuous time series of medium-resolution synthetic LAI mages with a Kalman filter We used a Kalman filter recursive algorithm (Kalman, 1960) to roduce a 30-m-resolution LAI time series at a 4-day time step sing the Landsat TM LAI data and the S-G filtered MODIS LAI time eries described in the…”
Neural Survival Recommender (2017, methodology): “This is usually accomplished by a latent variable model, such as a Hidden Markov Model [29] or a nonlinear Kalman Filter [17].”
Real-time aggregate monitoring with differential privacy (2012, methodology): “To improve the accuracy of data release at each time stamp, we propose a state space model and use of the Kalman filter [8].”
Deep learning in video multi-object tracking: A survey (2019, methodology): “They used a relatively simple approach that consisted in predicting object motion using the Kalman filter [36] and then associating the detections together with the help of the Hungarian algorithm [37], using intersection-over-union (IoU) distances to compute the cost matrix.”
FireFly Mosaic: A Vision-Enabled Wireless Sensor Networking System (2007, methodology): “Based on this linear Gaussian model, we use a Kalman filter [12] for the Simulator, to calculate an estimate of the Plant.”