Error sources and error propagation in the Levinson-Durbin algorithm

1993 ◽  
Vol 41 (4) ◽  
pp. 1635-1651 ◽  
Author(s):  
C.N. Papaodysseus ◽  
E.B. Koukoutsis ◽  
C.N. Triantafyllou
Keyword(s):  
Error Propagation ◽  
Error Sources

scholarly journals A Study on the Uncertainty of a Laser Triangulator Considering System Covariances

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1630
Author(s):  
Pablo Puerto ◽  
Beñat Estala ◽  
Alberto Mendikute
Keyword(s):  
Error Propagation ◽  
Measurement Accuracy ◽  
Maximum Error ◽  
Experimental Methodology ◽  
Joint Measurement ◽  
Error Sources ◽  
Processing Times ◽  
Main Error ◽  
Laser Plane ◽  
System Variables

A laser triangulation system, which is composed of a camera and a laser, calculates distances between objects intersected by the laser plane. Even though there are commercial triangulation systems, developing a new system allows the design to be adapted to the needs, in addition to allowing dimensions or processing times to be optimized; however the disadvantage is that the real accuracy is not known. The aim of the research is to identify and discuss the relevance of the most significant error sources in laser triangulator systems, predicting their error contribution to the final joint measurement accuracy. Two main phases are considered in this study, namely the calibration and measurement processes. The main error sources are identified and characterized throughout both phases, and a synthetic error propagation methodology is proposed to study the measurement accuracy. As a novelty in uncertainty analysis, the present approach encompasses the covariances of correlated system variables, characterizing both phases for a laser triangulator. An experimental methodology is adopted to evaluate the measurement accuracy in a laser triangulator, comparing it with the values obtained with the synthetic error propagation methodology. The relevance of each error source is discussed, as well as the accuracy of the error propagation. A linearity value of 40 µm and maximum error of 0.6 mm are observed for a 100 mm measuring range, with the camera calibration phase being the main error contributor.

A Novel Approach to Kinematic Reliability Analysis for Planar Parallel Manipulators

2020 ◽  
Vol 142 (8) ◽  
Author(s):  
Qiangqiang Zhao ◽  
Junkang Guo ◽  
Dingtang Zhao ◽  
Dewen Yu ◽  
Jun Hong
Keyword(s):  
Reliability Analysis ◽  
Error Propagation ◽  
Simulation Method ◽  
Parallel Manipulators ◽  
Plane Motion ◽  
Error Sources ◽  
Novel Approach ◽  
Safe Region ◽  
Motion Groups ◽  
Planar Parallel Manipulators

Abstract Kinematic reliability is an essential index that assesses the performance of the mechanism associating with uncertainties. This study proposes a novel approach to kinematic reliability analysis for planar parallel manipulators based on error propagation on plane motion groups and clipped Gaussian in terms of joint clearance, input uncertainty, and manufacturing imperfection. First, the linear relationship between the local pose distortion coming from the passive joint and that caused by other error sources, which are all represented by the exponential coordinate, are established by means of the Baker–Campbell–Hausdorff formula. Then, the second-order nonparametric formulas of error propagation on independent and dependent plane motion groups are derived in closed form for analytically determining the mean and covariance of the pose error distribution of the end-effector. On this basis, the kinematic reliability, i.e., the probability of the pose error within the specified safe region, is evaluated by a fast algorithm. Compared to the previous methods, the proposed approach has a significantly high precision for both cases with small and large errors under small and large safe bounds, which is also very efficient. Additionally, it is available for arbitrarily distributed errors and can analyze the kinematic reliability only regarding either position or orientation as well. Finally, the effectiveness and advantages of the proposed approach are verified by comparing with the Monte Carlo simulation method.

scholarly journals Error Propagation of Remote Sensing Rainfall Estimates in Soil Moisture Prediction from a Land Surface Model

2010 ◽  
Vol 11 (3) ◽  
pp. 705-720 ◽  
Author(s):  
Efthymios Serpetzoglou ◽  
Emmanouil N. Anagnostou ◽  
Anastasios Papadopoulos ◽  
Efthymios I. Nikolopoulos ◽  
Viviana Maggioni
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Error Propagation ◽  
Land Surface Model ◽  
Surface Model ◽  
Summer Period ◽  
Error Sources ◽  
Remote Sensing Techniques ◽  
Rainfall Estimates

Abstract The study presents an in-depth investigation of the properties of remotely sensed rainfall error propagation in the prediction of near-surface soil moisture from a land surface model (LSM). Specifically, two error sources are compared: rainfall forcing due to estimation error by remote sensing techniques and the representation of land–atmospheric processes due to LSM uncertainty [the Community Land Model, version 3.5 (CLM3.5), was used in this particular study]. CLM3.5 is forced by three remotely sensed precipitation products, namely, two satellite-based estimates—NASA’s Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis and NOAA’s Climate Prediction Center morphing technique (CMORPH)—and a rain gauge-adjusted radar–rainfall product from the Weather Surveillance Radar-1988 Doppler (WSR-88D) network. The error analysis is performed for the warm seasons of 2004 and 2006 and is facilitated through the use of in situ measurements of soil moisture, rainfall, and other meteorological variables from a network of stations capturing the state of Oklahoma (Oklahoma Mesonet). The study also presents a rigorous benchmarking of the Mesonet network as to its accuracy in deriving area rainfall estimates at the resolution of satellite products (0.25° and 3 h) through comparisons against the most definitive measurements of a smaller-yet-denser network of rain gauges in southwestern Oklahoma (Micronet). The study compares error statistics between modeling and precipitation error sources and between the various remote sensing techniques. Results are contrasted between the relatively moist summer period of 2004 to the drier summer period of 2006, indicating model and error propagation dependencies. An intercomparison between rainfall and modeling error shows that the two error sources are of similar magnitudes in the case of high modeling accuracy (i.e., 2004), whereas the contribution of rainfall forcing error to the uncertainty of soil moisture prediction can be lower when the model’s efficiency skill is relatively low (i.e., 2006).

Precision Analysis of Gravity Vertical Gradient Measurement Based on CG - 5 Relative Gravimeter

2011 ◽  
Vol 301-303 ◽  
pp. 1036-1041
Author(s):  
Qing Bin Wang ◽  
Rui Zhou ◽  
Wen Sun
Keyword(s):  
Error Propagation ◽  
Vertical Gradient ◽  
Precision Analysis ◽  
Error Sources ◽  
Accuracy Requirement ◽  
Gradient Measurement ◽  
Relative Gravimeter

CG-5 is new automatic land relative gravimeter developed by Canada Scintrex Company. Gravity vertical gradient can be measured by CG-5. This article mainly aims at precision analysis of the gravity vertical gradient measurement based on CG-5.This paper analyzes the error sources and error propagation formulae. In the end, it shows relation between altitude differences and observation sets in certain accuracy requirement. Results show that the precision of CG-5 vertical gradient measurement is improved when altitude difference and observation set increase. In vertical gradient measurement, 13 observation sets are needed with altitude difference at 2m to obtain the precision of ±20E.

scholarly journals Error propagation analysis of seven partial volume correction algorithms for [18F]THK-5351 brain PET imaging

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Senri Oyama ◽  
Ayumu Hosoi ◽  
Masanobu Ibaraki ◽  
Colm J. McGinnity ◽  
Keisuke Matsubara ◽  
...  
Keyword(s):  
Image Registration ◽  
Pet Imaging ◽  
Error Propagation ◽  
Volume Effect ◽  
Clinical Applications ◽  
Partial Volume Correction ◽  
Error Sources ◽  
Partial Volume ◽  
Volume Correction ◽  
Brain Pet

Abstract Background Novel partial volume correction (PVC) algorithms have been validated by assuming ideal conditions of image processing; however, in real clinical PET studies, the input datasets include error sources which cause error propagation to the corrected outcome. Methods We aimed to evaluate error propagations of seven PVCs algorithms for brain PET imaging with [18F]THK-5351 and to discuss the reliability of those algorithms for clinical applications. In order to mimic brain PET imaging of [18F]THK-5351, pseudo-observed SUVR images for one healthy adult and one adult with Alzheimer’s disease were simulated from individual PET and MR images. The partial volume effect of pseudo-observed PET images were corrected by using Müller-Gärtner (MG), the geometric transfer matrix (GTM), Labbé (LABBE), regional voxel-based (RBV), iterative Yang (IY), structural functional synergy for resolution recovery (SFS-RR), and modified SFS-RR algorithms with incorporation of error sources in the datasets for PVC processing. Assumed error sources were mismatched FWHM, inaccurate image-registration, and incorrectly segmented anatomical volume. The degree of error propagations in ROI values was evaluated by percent differences (%diff) of PV-corrected SUVR against true SUVR. Results Uncorrected SUVRs were underestimated against true SUVRs (− 15.7 and − 53.7% in hippocampus for HC and AD conditions), and application of each PVC algorithm reduced the %diff. Larger FWHM mismatch led to larger %diff of PVC-SUVRs against true SUVRs for all algorithms. Inaccurate image registration showed systematic propagation for most algorithms except for SFS-RR and modified SFS-RR. Incorrect segmentation of the anatomical volume only resulted in error propagations in limited local regions. Conclusions We demonstrated error propagation by numerical simulation of THK-PET imaging. Error propagations of 7 PVC algorithms for brain PET imaging with [18F]THK-5351 were significant. Robust algorithms for clinical applications must be carefully selected according to the study design of clinical PET data.

On Uncertainty Propagation in Mass, Damping and Stiffness Matrices Identification of Mechanical Systems

2012 ◽  
Author(s):  
Luis U. Medina ◽  
Sergio E. Díaz ◽  
Ningsheng Feng ◽  
Eric J. Hahn
Keyword(s):  
Error Propagation ◽  
Uncertainty Propagation ◽  
Mechanical Systems ◽  
Robust Algorithms ◽  
Error Sources ◽  
Suggested Approach ◽  
Stiffness Matrices ◽  
Accepted Practice ◽  
Noisy Measurements ◽  
Stiffness And Damping

The accuracy in estimating the mass, damping and stiffness matrices for mechanical systems depends on the error propagation through the stages involved in the parameter identification, i.e. excitation and response measurements, signal processing and modeling stages. Robust algorithms are available to estimate the system’s parameters in the presence of “noisy” measurements. However, uncertainties in the identified parameters of mechanical systems have not been usually reported or have simply been overlooked in the identification strategy. An overall uncertainty occurs for each identified parameter, and it may be defined in terms of error propagation. The recognition of relevant error contributions is the key to accomplishing parameter error estimation in the identification process, a task that may imply subtle aspects. An approach is proposed for uncertainty estimation in mass, stiffness and damping matrices for linearized mechanical systems. This approach is formulated as an extension of the accepted practice for evaluating experimental uncertainty for a scalar measurand. Typical error sources throughout the identification stages are also discussed. The suggested approach may be applied to identify mechanical systems in the frequency domain, and is independent of the algorithm used to estimate the system parameters. Practical limitations of the suggested approach are also discussed.

Analysis and Reduction of Errors in an Automated Robot-Based Repair Process Chain

2016 ◽  
Vol 840 ◽  
pp. 16-23
Author(s):  
Christoph Schwienbacher ◽  
Tomas Domaschke ◽  
Marc Andre Otto ◽  
Tobias Kötter ◽  
Thorsten Schüppstuhl
Keyword(s):  
Error Propagation ◽  
Data Transfer ◽  
Adaptive Methods ◽  
Repair Process ◽  
Industrial Robots ◽  
Quality Level ◽  
Process Chain ◽  
Error Sources ◽  
Complex Process ◽  
Robot Cell

Even though modern industrial robots have good repeatabilities, their positioning accuracies are still relatively poor. Moreover, in a complex process chain, involving several handling systems and diverse interdependent tasks, error propagation can make matters worse. In order to achieve the overall desired quality level, intelligent and highly adaptive methods are required to reduce individual errors and remove accuracy couplings as much as possible. This is especially true in high-risk applications, as found in the aviation MRO industry. Because of the difficulty to replicate existing manual MRO accuracy levels, automation in this area is still relatively scarce. For instance the inspection and repair of airplane combustion chamber liners are as yet performed fully manually. In this paper an automated version of the entire liner repair chain is introduced: from robot-guided white light interferometer inspection in a first cell, to part and data transfer to a second robot cell through to the automated repair steps. Particular consideration is given to individual error sources, such as robot and sensor inaccuracies, calibration deviations and the transfer of data between robot cells, as well as error propagation and prevention.

Sign in / Sign up

Export Citation Format

Share Document