Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches

By Dan Simon. 1995

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Citations in the corpus

A survey of data fusion in smart city applications (2019, methodology): “Common techniques under this category are Maximum Likelihood [62], Kalman Filter [63], Particle Filter [64], and”
Recent Advances on Recursive Filtering and Sliding Mode Design for Networked Nonlinear Stochastic Systems: A Survey (2013, background): “The gain-constrained filtering problem has been investigated for a broad class of real-time dynamical systems; see, for example, the tracking problem of a land based vehicle [103], the estimation problem of two state continuous stirred tank reactor [112], and the tracking problem of a vehicle along ”
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, background): “ 4.2(b) and recursive least squares can be used to ensure a fixed computational cost at each time step Simon (2006).”
A natural observer for optimal state estimation in second order linear structural systems (2011, background): “In that case the Kalman gain [8,14,21] is the optimal choice for the feedback matrix G(t), in the sense that it minimizes the trace of the state error covariance.”
On unscented Kalman filtering with state interval constraints (2010, methodology): “We thus extend approximate algorithms derived in the linear scenario to provide suboptimal estimates in the nonlinear case.”
Reduced-order Unscented Kalman Filtering with application to parameter identification in large-dimensional systems (2011, methodology): “However, UKF seems to have been – so far – restricted to systems of relatively limited sizes due to the lack of reduced-order versions for this approach, as commented on in [22].”
Observability Analysis for Dynamic State Estimation of Synchronous Machines (2017, methodology): “4) State error covariance (Q) associated with the UKF (Details are described in [13]) is chosen as: Q= 1e−4 × Im×m where m is the total number of the measurements.”
Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety (2011, unknown): “Then, it is straightforward to apply a general tracks management algorithm to create and delete the tracks, and use a Kalman filter to update their states [49].”
Novelty or Surprise? (2013, methodology): “Error correction is also central to the widely-used Kalman filter and related algorithms, where the error is called the “innovation” or “measurement residual” (Welch and Bishop, 1995).”
Pedestrian localisation for indoor environments (2008, methodology): “Bayesian filters provide a framework for probabilistically estimating the state of dynamic systems based on noisy measurements [52].”
State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model (2011, background): “It can be described as follows for a continuous-time system with discrete-time measurements [12], [18]: ⎧⎪⎨ ⎪⎩ ẋ = f(x,u,w, t) yk = h(xk,vk) w ∼ (0,Q) vk ∼ (0,Rk) (5) where u is the system input; w is the process noise and”