Presence Of Calibration Errors Research Articles

Optimal Transport Based Impulse Response Interpolation in the Presence of Calibration Errors

Optimal Transport Based Impulse Response Interpolation in the Presence of Calibration Errors

Assessing the impact of instrumental calibration uncertainty on LISA science

The primary scientific results of the future space-based gravitational wave interferometer LISA will come from the parameter inference of a large variety of gravitational wave sources. However, the presence of calibration errors could potentially degrade the measurement precision of the system parameters. Here, we assess the impact of calibration uncertainties on parameter estimation for individual sources, focusing on massive black holes, extreme-mass-ratio inspirals (EMRIs), galactic binaries, and stellar origin black hole binaries. Using a Fisher matrix formalism, we investigate how the measurement precision of source parameters degrades as a function of the size of the assumed calibration uncertainties. If we require that parameter measurements are degraded by no more than a factor of two relative to their value in the absence of calibration error, we find that calibration errors should be smaller than a few tenths of a percent in amplitude and $10^{-3}$ in phase. We also investigate the possibility of using verification binaries and EMRIs to constrain calibration uncertainties. Verification binaries can constrain amplitude calibration uncertainties at the level of a few percent, while both source types can provide constrain phase calibration at the level of a few$\times10^{-2}$.

Effects of Item Calibration Errors on Computerized Adaptive Testing under Cognitive Diagnosis Models

In a cognitive diagnostic computerized adaptive testing (CD-CAT) exam, an item pool that consists of items with calibrated item parameters is used for item selection and attribute estimation. The parameter estimates for the items in the item pool are often treated as if they were the true population parameters, and therefore, the calibration errors are ignored. The purpose of this study was to investigate the effects of calibration errors on the attribute classification accuracy, the measurement precision of attribute mastery classification, and the test information under the log-linear cognitive diagnosis model (LCDM) framework. The deterministic input, noisy “and” gate (DINA) model and the compensatory re-parameterized unified model (C-RUM) were used in fixed-length CD-CAT simulations. The results showed that high levels of calibration errors were associated with low classification accuracy, low test information, and misleading estimation of measurement precision. The effects of calibration errors decreased as the test length increased, and the DINA model appeared to be more vulnerable in the presence of calibration errors. The C-RUM was less influenced by calibration errors because of its additive characteristics in the LCDM framework. The same conclusions applied when item exposure control was incorporated and when different item selection methods were used. Finally, the use of a larger calibration sample size to calibrate the item pool was found to reduce the magnitudes of error variances and increase the attribute classification accuracy.

Deterministic skill of ENSO predictions from the North American Multimodel Ensemble

Hindcasts and real-time predictions of the east-central tropical Pacific sea surface temperature (SST) from the North American Multimodel Ensemble (NMME) system are verified for 1982–2015. Skill is examined using two deterministic verification measures: mean squared error skill score (MSESS) and anomaly correlation. Verification of eight individual models shows somewhat differing skills among them, with some models consistently producing more successful predictions than others. The skill levels of MME predictions are approximately the same as the two best performing individual models, and sometimes exceed both of them. A decomposition of the MSESS indicates the presence of calibration errors in some of the models. In particular, the amplitudes of some model predictions are too high when predictability is limited by the northern spring ENSO predictability barrier and/or when the interannual variability of the SST is near its seasonal minimum. The skill of the NMME system is compared to that of the MME from the IRI/CPC ENSO prediction plume, both for a comparable hindcast period and also for a set of real-time predictions spanning 2002–2011. Comparisons are made both between the MME predictions of each model group, and between the average of the skills of the respective individual models in each group. Acknowledging a hindcast versus real-time inconcsistency in the 2002–2012 skill comparison, the skill of the NMME is slightly higher than that of the prediction plume models in all cases. This result reflects well on the NMME system, with its large total ensemble size and opportunity for possible complementary contributions to skill.

Transfer-Function-Based Calibration of Sparse EEG Systems for Brain Source Localization

This paper describes a transfer-function-based calibration technique for a sparse electroencephalography (EEG) source localization system. The forward solution and synthetic EEG signals are generated using the Brainstorm software. Various forms of calibration errors have been defined and induced on the nominal manifold, with different magnitudes. Two transfer-function-based calibration techniques are presented and the improvement brought about using these methods are compared against the results from benchmark scenarios. The performance of the proposed calibration techniques has been quantified in terms of the localization errors in the recovered sources before and after calibrating the system. Simulations suggest that in the presence of calibration errors, the proposed technique is capable of reducing the localization error as well as the number of falsely recovered sources.

A-Stratified Computerized Adaptive Testing in the Presence of Calibration Error.

a-Stratified computerized adaptive testing with b-blocking (AST), as an alternative to the widely used maximum Fisher information (MFI) item selection method, can effectively balance item pool usage while providing accurate latent trait estimates in computerized adaptive testing (CAT). However, previous comparisons of these methods have treated item parameter estimates as if they are the true population parameter values. Consequently, capitalization on chance may occur. In this article, we examined the performance of the AST method under more realistic conditions where item parameter estimates instead of true parameter values are used in the CAT. Its performance was compared against that of the MFI method when the latter is used in conjunction with Sympson-Hetter or randomesque exposure control. Results indicate that the MFI method, even when combined with exposure control, is susceptible to capitalization on chance. This is particularly true when the calibration sample size is small. On the other hand, AST is more robust to capitalization on chance. Consistent with previous investigations using true item parameter values, AST yields much more balanced item pool usage, with a small loss in the precision of latent trait estimates. The loss is negligible when the test is as long as 40 items.

Impact of calibration errors on CMB component separation using FastICA and ILC

The separation of emissions from different astrophysical processes is an important step towards the understanding of observational data. This topic of component separation is of particular importance in the observation of the relic Cosmic Microwave Background Radiation, as performed by the WMAP satellite and the more recent Planck mission, launched May 14th, 2009 from Kourou and currently taking data. When performing any sort of component separation, some assumptions about the components must be used. One assumption that many techniques typically use is knowledge of the frequency scaling of one or more components. This assumption may be broken in the presence of calibration errors. Here we compare, in the context of imperfect calibration, the recovery of a clean map of emission of the Cosmic Microwave Background from observational data with two methods: FastICA (which makes no assumption of the frequency scaling of the components), and an `Internal Linear Combination' (ILC), which explicitly extracts a component with a given frequency scaling. We find that even in the presence of small calibration errors with a Planck-style mission, the ILC method can lead to inaccurate CMB reconstruction in the high signal-to-noise regime, because of partial cancellation of the CMB emission in the recovered map. While there is no indication that the failure of the ILC will translate to other foreground cleaning or component separation techniques, we propose that all methods which assume knowledge of the frequency scaling of one or more components be careful to estimate the effects of calibration errors.

Improvements on Visual Servoing From Spherical Targets Using a Spherical Projection Model

This paper is concerned with improvements to visual feature modeling using a spherical projection model. Three spherical targets are considered: a sphere, a sphere marked with a tangent vector to a point on its surface, and a sphere marked with two points on its surface. A new minimal and decoupled set of visual features is proposed for each target using any central catadioptric camera. Using the proposed set for a sphere, a classical control law is proved to be globally asymptotically stable in the presence of modeling errors and locally asymptotically stable in the presence of calibration errors, considering that perspective and paracatadioptric cameras were used. Simulation and experimental results with perspective, paracatadioptric, and fish-eye cameras validate the proposed theoretical results.

Motion bias and structure distortion induced by intrinsic calibration errors

This article provides an account of sensitivity and robustness of structure and motion recovery with respect to the errors in intrinsic parameters of the camera. We demonstrate both analytically and in simulation, the interplay between measurement and calibration errors and their effect on motion and structure estimates. In particular we show that the calibration errors introduce an additional bias towards the optical axis, which has opposite sign to the bias typically observed by egomotion algorithms. The overall bias causes a distortion of the resulting 3D structure, which we express in a parametric form. The analysis and experiments are carried out in the differential setting for motion and structure estimation from image velocities. While the analytical explanations are derived in the context of linear techniques for motion estimation, we verify our observations experimentally on a variety of optimal and suboptimal motion and structure estimation algorithms. The obtained results illuminate and explain the performance and sensitivity of the differential structure and motion recovery techniques in the presence of calibration errors.

Rainfall rate retrieval in presence of path attenuation using C-band polarimetric weather radars

Abstract. Weather radar systems are very suitable tools for the monitoring of extreme rainfall events providing measurements with high spatial and temporal resolution over a wide geographical area. Nevertheless, radar rainfall retrieval at C-band is prone to several error sources, such as rain path attenuation which affects the accuracy of inversion algorithms. In this paper, the so-called rain profiling techniques (namely the surface reference method FV and the polarimetric method ZPHI) are applied to correct rain path attenuation and a new neural network algorithm is proposed to estimate the rain rate from the corrected measurements of reflectivity and differential reflectivity. A stochastic model, based on disdrometer measurements, is used to generate realistic range profiles of raindrop size distribution parameters while a T-matrix solution technique is adopted to compute the corresponding polarimetric variables. A sensitivity analysis is performed in order to evaluate the expected errors of these methods. It has been found that the ZPHI method is more reliable than FV, being less sensitive to calibration errors. Moreover, the proposed neural network algorithm has shown more accurate rain rate estimates than the corresponding parametric algorithm, especially in presence of calibration errors.

Optimal extraction of cosmological information from supernova data in the presence of calibration uncertainties

We present a new technique to extract the cosmological information from high-redshift supernova data in the presence of calibration errors and extinction due to dust. While in the traditional technique the distance modulus of each supernova is determined separately, in our approach we determine all distance moduli at once, in a process that achieves a significant degree of self-calibration. The result is a much reduced sensitivity of the cosmological parameters to the calibration uncertainties. As an example, for a strawman mission similar to that outlined in the SNAP satellite proposal, the increased precision obtained with the new approach is roughly equivalent to a factor of five decrease in the calibration uncertainty.