Dynamic modelling of displacements on an embankment dam using the Kalman filter

Gamse, Sonja

doi:10.1080/14498596.2017.1330711

Cited by 8 publications

(5 citation statements)

References 9 publications

(17 reference statements)

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“…The performed analysis utilizes the Kalman filtering method. General assumptions regarding this method and its application in geodetic measurement result processing are discussed in [12][13][14][15][16]. In the paper, Kalman filtering is implemented to predict sensor displacements.…”

Section: Methodsmentioning

confidence: 99%

The Concept of Geodetic Analyses of the Measurement Results Obtained by Hydrostatic Leveling

Kamiński

Makowska

2019

Geosciences

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The article discusses the issue of hydrostatic leveling. Its application is presented in structural health monitoring systems in order to determine vertical displacements of controlled points. Moreover, the article includes a complete computation scheme that utilizes the estimation from observation differences, allowing the elimination of the influence of individual sensors’ systematic errors. The authors suggest two concepts of processing the measurement results depending on the sensors’ connection method. Additionally, the second concept is extended by the elements allowing the prediction of the displacements by means of Kalman filtering.

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Section: Methodsmentioning

confidence: 99%

The Concept of Geodetic Analyses of the Measurement Results Obtained by Hydrostatic Leveling

Kamiński

Makowska

2019

Geosciences

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“…Here the vector 𝒙 𝑘 contains the unknown sensor biases in addition to the state variables representing the humidity. Unfortunately, the determination of 𝒙 𝑘 by sensor fusion is impossible since the matrix pair (𝑭𝑭, 𝑯) is unobservable [27], [28]. Let us try to find a solution to this problem by treating the sum of the actual humidity and the bias of one of the sensors, e.g.…”

Section: Fusion Of Humidity Sensorsmentioning

confidence: 99%

Application of Polynomial Models in Bayesian Fusion of Humidity Sensors

Nikovski,

Doychinov

2023

TEM Journal

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Local polynomial trend models are a special class of state-space models that can be used without having the full information about the process under study, since most of their parameters are embodied in the state vector and estimated immediately. This makes them attractive for use in signal processing. The present work considers problems that arise when using a polynomial model with a local quadratic trend for Bayesian fusion of two humidity sensors. The unknown sensor biases make it impossible for the model to satisfy the observability conditions. There is currently no general solution to this problem. To overcome this difficulty, an approach is presented where the humidity measurement result implicitly includes the bias of one of the sensors. The results of the study can be used to fuse quantities other than humidity when two or more sensors are available.

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“…where the matrix 𝑄 is the covariance of the model error 𝒘 𝑘 . If a white noise acceleration model is assumed [14], [25], [26], the 𝑄 matrix is equal to…”

Section: A the Prediction Processmentioning

confidence: 99%

“…It is currently used in several fields, and in a wide range of applications [10], [11], such as orbit calculation, target tracking and navigation. In landslide monitoring the application of KF techniques has recently gained attention, as it proved to be effective in estimating and forecasting ground displacements [12]- [14]. This technique has been applied to displacement measurements acquired with different sensors, and successfully managed to filter out noise and properly predict ground displacement.…”

Section: Introductionmentioning

confidence: 99%

Kalman Filter Application to GBSAR Interferometry for Slope Monitoring

2022

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The use of Kalman filtering techniques for landslide monitoring has proved effective as a tool for estimating and predicting land displacements. Ground-Based Synthetic Aperture Radars (GBSAR) are popular remote sensing instruments able to provide displacement maps of the investigated area, with submillimeter precision. These instruments outperform other sensors in several respects, such as all-weather and all-day monitoring. However, in some cases, for instance in vegetated scenarios, the displacement is affected by a significant uncertainty due to the decorrelation of the radar signal. In such a case, to retrieve any reliable information, noise must be filtered out using appropriate methods. Given the success of kinematic modeling of landslide movements through Kalman filtering, this technique seems to be the optimal candidate for processing the displacement measured by interferometric GBSAR data. This paper investigates this idea, by applying Kalman Filter to GBSAR measurements acquired in two different campaigns: a landslide monitoring in north Spain, and a sliding glacier monitoring in the Alpes, Italy. A proper initialization of the algorithm parameters is fundamental for a correct application of the Kalman filter. In this work, we present a strategy that exploits information from coherent pixels for tuning the filtering parameters and optimizing the filter performance on areas with low coherence.

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Dynamic modelling of displacements on an embankment dam using the Kalman filter

Cited by 8 publications

References 9 publications

The Concept of Geodetic Analyses of the Measurement Results Obtained by Hydrostatic Leveling

The Concept of Geodetic Analyses of the Measurement Results Obtained by Hydrostatic Leveling

Application of Polynomial Models in Bayesian Fusion of Humidity Sensors

Kalman Filter Application to GBSAR Interferometry for Slope Monitoring

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