On Kalman Filtering With Nonlinear Equality Constraints

By Simon Justin Julier, J. Laviola. IEEE Transactions on Signal Processing, 2007

324 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/672205e45e77b6474c08acd8c54ded2f87e8d619

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Citations in the corpus

Methods for Sparse Signal Recovery Using Kalman Filtering With Embedded Pseudo-Measurement Norms and Quasi-Norms (2010, methodology): “The constrained optimization problem (7) can be solved in the framework of Kalman filtering using the pseudo-measurement (PM) technique [12].”
State and parameter estimation of hydrologic models using the constrained ensemble Kalman filter (2009, background): “For equality constraints, constraints on the entire distribution subsume the constraints on the mean only if the equality constraint is linear [Julier and LaViola, 2007].”
Covariance Correction Step for Kalman Filtering with an Attitude (2017, methodology): “Alternative methods, such as the use of more than three dynamic states combined with constraints incur additional theoretical and computational complexity, see [4, 5] for examples.”
Recent Advances on Recursive Filtering and Sliding Mode Design for Networked Nonlinear Stochastic Systems: A Survey (2013, background): “It should be pointed out that the filtering problems with constraints have been gaining a recurring research interest in the past decade; see, for example, [103, 107–111].”
Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration (2011, background): “There are several possible ways to enforce the unit norm constraint within the UKF, for example by incorporating pseudo-measurements, or by projecting the unconstrained time and measurement updates onto the quaternion constraint surface (Julier and LaViola 2007).”
On unscented Kalman filtering with state interval constraints (2010, methodology): “Model information is used during the forecast step, while measurement data are injected into the estimates during the data-assimilation step.”
Using Inertial Sensors for Position and Orientation Estimation (2017, unknown): “Different approaches to handle the normalization of the quaternions in these algorithms are discussed in [67].”