Optimal Estimation of Dynamic Systems

By John L. Crassidis, John L. Junkins. 2004

1,355 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a2eaa37a55779d296199c28645cda4fcfb1fb4ce

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Citations in the corpus

Wearout Effects of Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales Relationship (2007, methodology): “The justification for use of the semilog specification has been presented in Doyle and Saunders (1990). They rule out linear and exponential functional forms as they do not exhibit diminishing returns, which is a characteristic of advertising response models (Simon 1970).”
Predicting the Earth encounters of (99942) Apophis (2008, background): “A weighted least-squares estimate of orbital elements is self-correcting in that the sum of the squares of the weighted differences between each actual measurement and a predicted measurement (the “observed minus computed” residuals) is minimized by correcting the estimated position and velocity of ”
Fusing Physics-based and Deep Learning Models for Prognostics (2020, methodology): “In fact, UKF is widely applied for purposes of aircraft engine health evaluation [14, 28,31].”
Relative Navigation of Air Vehicles (2008, methodology): “In formulating the cost function, the information form of the covariance update will be used [32]:”
Kalman filtering for relative spacecraft attitude and position estimation (2005, background): “A review of the EKF equations can be found in [23].”
State of charge estimation using an unscented filter for high power lithium ion cells (2010, background): “In addition to this, the extended filter algorithm is often reported to have poor convergence, if the initial guess values are not sufficiently close [44].”
Expected Policy Gradients for Reinforcement Learning (2018, background): “First, discrete-time linear quadratic regulators (LQR) with time-varying feedback, a class of problems widely studied in classical control theory, are known to have a Q -function that is quadric in the action vector given the state (Crassidis and Junkins, 2011, Equation 8.81) 6 .”
Observer/Kalman-Filter Time-Varying System Identification (2010, methodology): “Linear Estimators of the Kalman Type: A Review of the Structure and Properties We review the structure and properties of the state estimators for linear discrete time varying dynamical systems (Kalman Filter Theory[22, 23]) using the innovations approach propounded by Kailath[24] and Mehra[25].”
Bayesian Filtering and Smoothing (2013, methodology): “Target tracking (Bar-Shalom et al., 2001; Crassidis and Junkins, 2004; Challa et al., 2011) refers to the methodology where a set of sensors such as active or passive radars, radio frequency sensors, acoustic arrays,”