Application of Kalman filtering to real-time preprocessing of geophysical data

Noriega, Gerardo; Pasupathy, S.

doi:10.1109/36.175324

Cited by 21 publications

(12 citation statements)

References 15 publications

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“…In contrast to most existing filtering methods, Kalman filters are well suited to work with variable sampling intervals, as demonstrated for two-state models in SABATINI (1995). Also, their computational complexity is compatible with real-time requirements in most applications (NoRIEGA and PASUPATHY, 1992;CORRADIN~ et al, 1993;SABATINI, 1995) incurred by working with a centred method of computing finite differences (two-sample lag when the median smoother is inserted) is the modest price paid in a change for its superiority over the backward methods, in terms of either truncation error levels (PRESS et al, 1992) or measurement noise magnification.…”

Section: Discussionmentioning

confidence: 93%

Real-time kalman filter applied to biomechanical data for state estimation and numerical differentiation

Sabatini

2003

Med. Biol. Eng. Comput.

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This study focused on the application of real-time Kalman filters to biomechanical data and, in particular, the simulation environment used to compare the performance of modified and standard two-state Kalman filters when estimating displacement and velocity from noisy displacement data. The modification proposed in this paper was the numerical tachometer, augmented by a median smoother. The numerical tachometer integrated the derivative estimates from finite differences of noisy sampled data into the Kalman filter structure; the median smoother acted before differentiation, to protect from grossly erroneous measurements. The numerical tachometer allowed better fits to the simulated data than can be achieved without it: the root mean square errors decreased by 10% in the displacement domain and by 54% in the velocity domain, for sampling frequencies and signal contamination levels that were typical in human movement sciences. The sensitivity to errors in the modelling of the signal and noise characteristics was less than in the standard filter implementation. The use of the median smoother improved the robustness of the filtering algorithm against additive white Gaussian measurement noise and allowed the cancellation of isolated noise spikes.

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Section: Discussionmentioning

confidence: 93%

Real-time kalman filter applied to biomechanical data for state estimation and numerical differentiation

Sabatini

2003

Med. Biol. Eng. Comput.

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“…computer visual systems), or when the observations are not always available (GPS position data), it is still possible to use a Kalman Filter considering that while new observations are not available, only the prediction phase will be executed; i.e. estimation phase is not executed until new observed data are available [6]. The same procedure can be adopted when observations or measurements are not reliable [6].…”

Section: Execution Monitoring Applied To Data Estimation Processesmentioning

confidence: 98%

“…estimation phase is not executed until new observed data are available [6]. The same procedure can be adopted when observations or measurements are not reliable [6]. So, detecting sensor measurement faults (i.e.…”

Section: Execution Monitoring Applied To Data Estimation Processesmentioning

confidence: 99%

Execution Monitoring Applied to Data Estimation Processes

Freire

Carvalho

Cardoso

et al. 2008

2008 IEEE Latin American Robotic Symposium

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“…comparison is performed by the "Decision When a fault is detected, the "Decision signalizes the "Estimation" block that observation vector is not reliable. In this cas phase of the α-β Filter is not executed, an obtained in the output of the prediction phase the output vector of parameters [5] [16].…”

Section: The Fault-tolerant Estimation Processmentioning

confidence: 99%

“…(17) Combining (16) and (17) the optimal values of the α-β parameters may be explicitly obtained in terms of Λ, by:…”

Section: The Fault-tolerant Estimation Processmentioning

confidence: 99%

Fault-tolerant weld line detection for automatic inspection of storage tanks based on distance and visual information fusion

Carvalho

Luciano

Freire

et al. 2009

2009 IEEE Intrumentation and Measurement Technology Conference

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Quality control, cost reduction and above all, human and environmental safety are great reasons that stimulate the investments in technologies like automatic inspection. The automatic inspection of weld lines in storage tanks is of special interest, due to the fact that such tanks are currently used to store harmful products. For a reliable inspection it is necessary to accurate detect the weld line position. In this paper the development of a system to perform weld line detection in storage tanks is proposed. Two redundant systems, based on different physical principles, distance and visual information, are implemented and tested. Such systems make use of a fault-tolerant estimation process based on the α-β filter. Finally, the outputs of the two redundant systems are fused, aiming at to increase the confidence and performance of the system.

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Application of Kalman filtering to real-time preprocessing of geophysical data

Cited by 21 publications

References 15 publications

Real-time kalman filter applied to biomechanical data for state estimation and numerical differentiation

Real-time kalman filter applied to biomechanical data for state estimation and numerical differentiation

Execution Monitoring Applied to Data Estimation Processes

Fault-tolerant weld line detection for automatic inspection of storage tanks based on distance and visual information fusion

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