Constrained Kalman filtering using pseudo-measurements

Richards, Phillip W.

doi:10.1049/ic:19950676

Cited by 22 publications

(9 citation statements)

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“…One method of introducing state constraints is to use pseudo-measurements. 5 The fundamental idea is to introduce a perfect measurement (hence the use of the term "pseudomeasurement") consisting of the constraint equation into the estimation solution. This approach has shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Norm-Constrained Kalman Filtering

Zanetti

Majji

Bishop

et al. 2009

Journal of Guidance, Control, and Dynamics

105

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The problem of estimating the state vector of a dynamical system from vector measurements, when it is known that the state vector satisfies norm equality constraints is considered. The case of a linear dynamical system with linear measurements subject to a norm equality constraint is discussed with a review of existing solutions. The norm constraint introduces a nonlinearity in the system for which a new estimator structure is derived by minimizing a constrained cost function. It is shown that the constrained estimate is equivalent to the brute force normalization of the unconstrained estimate. The obtained solution is extended to nonlinear measurement models and applied to the spacecraft attitude filtering problem.

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Section: Introductionmentioning

confidence: 99%

Norm-Constrained Kalman Filtering

Zanetti

Majji

Bishop

et al. 2009

Journal of Guidance, Control, and Dynamics

105

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“…13, the measurement is set to z k = 0 and measurement model is set to h(q k ) = Hq k . Since z k = 0 is not a true measurement, it is often referred to as pseudo-measurement in literature [23]. Thus Eq.…”

Section: A Position Measurementsmentioning

confidence: 99%

Bingham Distribution-Based Linear Filter for Online Pose Estimation

Rangaprasad¹,

Xu²,

Zevallos-Roberts³

et al. 2017

Robotics: Science and Systems XIII

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Abstract-Pose estimation is central to several robotics applications such as registration, hand-eye calibration, SLAM, etc. Online pose estimation methods typically use Gaussian distributions to describe the uncertainty in the pose parameters. Such a description can be inadequate when using parameters such as unit-quaternions that are not unimodally distributed. A Bingham distribution can effectively model the uncertainty in unit-quaternions, as it has antipodal symmetry and is defined on a unit-hypersphere. A combination of Gaussian and Bingham distributions is used to develop a linear filter that accurately estimates the distribution of the pose parameters, in their true space. To the best of our knowledge our approach is the first implementation to use a Bingham distribution for 6 DoF pose estimation. Experiments assert that this approach is robust to initial estimation errors as well as sensor noise. Compared to state of the art methods, our approach takes fewer iterations to converge onto the correct pose estimate. The efficacy of the formulation is illustrated with a number of simulated examples on standard datasets as well as real-world experiments.

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“…If a boundary has been crossed, a pseudo-measurement update is performed to force the solution to the known range of values. This method of applying inequality constraints to an extended Kalman filter adds a minimal amount of information to the filter to achieve the constraint (Richards 1995).…”

Section: Description Of the Measurements Errors And Positioning Filtermentioning

confidence: 99%

Accuracy and reliability of tightly coupled GPS/ultra-wideband positioning for surveying in urban environments

2010

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A tightly coupled filter integrating GPS and ultra-wideband (UWB) observations for high-precision position applications is implemented and tested to survey several external corner points of an eight story building. The filter uses GPS pseudoranges, GPS carrier-phase measurements, and UWB ranges and includes in-run estimation of UWB bias and scale factor states. The filter employs inequality constraints and innovation testing to mitigate the effects of unmodeled errors. The tightly coupled solution is compared to GPS-only, UWB-only, and loosely coupled solutions. The ability of each solution to detect measurement blunders is compared. Sub-meter level position solutions are maintained using tight-coupling in conditions where the solutions from the other three approaches are either unavailable or unreliable.

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Constrained Kalman filtering using pseudo-measurements

Cited by 22 publications

References 0 publications

Norm-Constrained Kalman Filtering

Norm-Constrained Kalman Filtering

Bingham Distribution-Based Linear Filter for Online Pose Estimation

Accuracy and reliability of tightly coupled GPS/ultra-wideband positioning for surveying in urban environments

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