Observational Uncertainties Research Articles

A Novel Sensor Integration Scheme for an Aided Inertial Navigation System Based on a Generalized PID Filter in the Presence of Observation Uncertainty

A Novel Sensor Integration Scheme for an Aided Inertial Navigation System Based on a Generalized PID Filter in the Presence of Observation Uncertainty

Reconstructing Sunspot Number by Forward-Modelling Open Solar Flux

The open solar flux (OSF) is the integrated unsigned magnetic flux leaving the top of the solar atmosphere to form the heliospheric magnetic field. As the OSF modulates the intensity of galactic cosmic rays at Earth, the production rate of cosmogenic isotopes – such as 14C and 10Be stored in tree rings and ice sheets – is closely related to the OSF. Thus on the basis of cosmogenic isotope data, OSF can be reconstructed over millennia. As sunspots are related to the production of OSF, this provides the possibility of reconstructing sunspot number (SSN) and hence properties of the solar cycles prior to the first sunspot telescopic observations in 1610. However, while models exist for estimating OSF on the basis of SSN, the hysteresis present in OSF and the lack of a priori knowledge of the start/end dates of individual solar cycles means that directly inverting these models is not possible. We here describe a new method that uses a forward model of OSF to estimate SSN and solar cycle start/end dates through a Monte Carlo approach. The method is tested by application to geomagnetic reconstructions of OSF over the period 1845-present, and compared to the known SSN record for this period. There is a substantial improvement in reconstruction of both the SSN time series and the solar cycle start/end dates compared with existing OSF-SSN regression methods. This suggests that more accurate solar-cycle information can be extracted from cosmogenic isotope records by forward modelling, and also provides a means to assess the level of agreement between independent SSN and OSF reconstructions. We find the geomagnetic OSF and observed SSN agree very well after 1875, but do differ during the early part of the geomagnetic record, though still agree within the larger observational uncertainties.

GNSS/UWB integrated positioning with robust Helmert variance component estimation

A robust GNSS/UWB integration algorithm is proposed for the indoor-outdoor cross region to control the influences of observation outliers of both GNSS and UWB. An important attempt in this paper is to re-estimate the uncertainty of the virtual observations derived from the kinematic model predicted information, and the GNSS satellites as well as the UWB anchors observations by using variance component estimation. Due to the correlations of the derived observations from estimated state parameters at previous epoch, a bi-factor inflated covariance model is employed to reduce the influences of the predicted correlated observations. Accordingly, a signal variance inflation factor is employed for independent measurements. Semi-simulation and field tests are carried out to validate and analyze the performance of the proposed algorithms in terms of positioning accuracy. The simulation demonstrates that the proposed algorithm improves the positioning accuracy when outliers exist in the measurements and the correlated predicted information. The field test results show that the positioning accuracy by using the proposed methods is improved by about 56% in average of E, N, and U components compared to that of the least squares positioning strategy, and about 40% in the average of E, N, and U components compared to that of integrated positioning by EKF without companying the variance component estimation.

Asymptotic consensus of multi-agent systems under binary-valued observations and observation uncertainty

Asymptotic consensus of multi-agent systems under binary-valued observations and observation uncertainty

Observational Uncertainty for Global Drought‐Pluvial Volatility

AbstractDroughts and pluvials have occurred in most regions in the past. However, what calls growing attention is the additive effects of these two opposite extreme events occurring in spatial‐temporal proximity to one another, sometimes beyond either of the hazards individually. This study examines the likelihood of global drought‐pluvial volatility which involves both meteorological drought‐to‐pluvial (DTP) and pluvial‐to‐meteorological drought transitions; meanwhile, identifies discrepancies and agreements among the widely used observations for such events, an aspect that remains currently overlooked. Globally, we find that the observation‐based data sets including Global Precipitation Climatology Center (GPCC), Climate Research Unit (CRU) and ERA5 reach a good agreement in estimating the event transition rates, with an average 15.46% (15.49%) of all meteorological droughts (pluvials) being succeeded by pluvials (meteorological droughts) in the following season. At the regional scale, our results reveal that the spatial variability and frequency associated with meteorological DTP transitions are slightly larger than that with pluvial‐to‐ meteorological drought transitions, but the observational uncertainty is more pronounced in the latter case as a result of greater uncertainty in the univariate drought depiction and enhanced regional divergence among the observed data. In general, GPCC and CRU exhibit higher consistency, albeit with less agreement under pluvial‐to‐ meteorological drought transitions, while ERA5 yields underestimations and reduced spatial variability considering both transition scenarios. The study highlights a need of using multiple independent observation‐based data sets for compound/multivariate extreme analysis, particularly in the context of climate‐related decision‐making, water resources planning, and future model validation studies.

Revisiting coupled CDM-massive neutrino perturbations in diverse cosmological backgrounds

Massive neutrinos are well-known to cause a characteristic suppression in the growth of structures at scales below the neutrino free-streaming length. A detailed understanding of this suppression is essential in the era of precision cosmology we are entering into, enabling us to better constrain the total neutrino mass and possibly probe (beyond)-ΛCDM cosmological model(s). Instead of the usual N-body simulation or Boltzmann solver, in this article we consider a two-fluid framework at the linear scales, where the neutrino fluid perturbations are coupled to the CDM (+ baryon) fluid via gravity at redshifts of interest. Treating the neutrino mass fraction f ν as a perturbative parameter, we find solutions to the system with redshift-dependent neutrino free-streaming length in ΛCDM background via two separate approaches. The perturbative scale-dependent solution is shown to be in excellent agreement with numerical solution of the two-fluid equations valid to all orders in f ν, and also agrees with results from CLASS to a good accuracy. We further generalize the framework to incorporate different evolving dark energy backgrounds and found sub-percent level differences in the suppression, all of which lie within the observational uncertainty of BOSS-like surveys. We also present a brief discussion on the prospects of the current analysis in the context of upcoming missions.

Mitigation of multiple outliers using consistency checking for GNSS standard point positioning in urban areas

High-precision positioning in urban environments is still a challenging task for global navigation satellite system (GNSS) as the observations are susceptible to outliers. Receiver autonomous integrity monitoring (RAIM) has been widely used in GNSS positioning to resist the outliers. However, conventional RAIM typically assumes only a single outlier, when multiple outliers are present, the method’s robustness can be adversely affected and a time-consuming iterative processing procedure is often required. To process multiple outliers more robustly and efficiently, we propose a consistency-checking approach based on the concept of random sample consensus (RANSAC). Modifications are made to enhance the RANSAC algorithm, including the use of spatial and temporal continuity of positioning geometry to accelerate RANSAC, and adaptive determination of the inlier-outlier threshold based on observation and parameter uncertainty. The effectiveness and efficiency of the proposed method are validated in GNSS single point positioning (SPP) in both a simulation and a real driving test.

Simulations of ocean deoxygenation in the historical era: insights from forced and coupled models

Ocean deoxygenation due to anthropogenic warming represents a major threat to marine ecosystems and fisheries. Challenges remain in simulating the modern observed changes in the dissolved oxygen (O2). Here, we present an analysis of upper ocean (0-700m) deoxygenation in recent decades from a suite of the Coupled Model Intercomparison Project phase 6 (CMIP6) ocean biogeochemical simulations. The physics and biogeochemical simulations include both ocean-only (the Ocean Model Intercomparison Project Phase 1 and 2, OMIP1 and OMIP2) and coupled Earth system (CMIP6 Historical) configurations. We examine simulated changes in the O2 inventory and ocean heat content (OHC) over the past 5 decades across models. The models simulate spatially divergent evolution of O2 trends over the past 5 decades. The trend (multi-model mean and spread) for upper ocean global O2 inventory for each of the MIP simulations over the past 5 decades is 0.03 ± 0.39×1014 [mol/decade] for OMIP1, −0.37 ± 0.15×1014 [mol/decade] for OMIP2, and −1.06 ± 0.68×1014 [mol/decade] for CMIP6 Historical, respectively. The trend in the upper ocean global O2 inventory for the latest observations based on the World Ocean Database 2018 is −0.98×1014 [mol/decade], in line with the CMIP6 Historical multi-model mean, though this recent observations-based trend estimate is weaker than previously reported trends. A comparison across ocean-only simulations from OMIP1 and OMIP2 suggests that differences in atmospheric forcing such as surface wind explain the simulated divergence across configurations in O2 inventory changes. Additionally, a comparison of coupled model simulations from the CMIP6 Historical configuration indicates that differences in background mean states due to differences in spin-up duration and equilibrium states result in substantial differences in the climate change response of O2. Finally, we discuss gaps and uncertainties in both ocean biogeochemical simulations and observations and explore possible future coordinated ocean biogeochemistry simulations to fill in gaps and unravel the mechanisms controlling the O2 changes.

Remaining useful life prediction using nonlinear multi-phase Wiener process and variational Bayesian approach

Due to the changeable internal mechanisms or external working conditions, the degradation trend of products usually presents multiple phase characteristics. However, most existing multi-phase degradation methods treat each phase as linear and rely on artificial designation to directly specify the forms of drift models, which may result in an inaccurate description of degradation characteristics for complex equipment. To this end, we formulate a general nonlinear multi-phase degradation model with three-source variability based on the Wiener process. Meanwhile, a stage division method is developed to automatically determine the degradation phase number, change-point locations, and the forms of drift models. Then, we obtain the expressions of remaining useful life (RUL) by considering the uncertainty of change-point degradation observations. Especially, we derive the approximate analytical solution of RUL based on the linear model. Furthermore, to fully consider the unit-to-unit heterogeneity and utilize the degradation observations of the in-service unit and prior information simultaneously, we propose a parameter estimation method based on variational Bayesian approach, which adaptively updates all parameters as random variables. Finally, two numerical examples and three practical examples are provided to verify the effectiveness of the proposed method.

Estimating Individual Radio Occultation Uncertainties Using the Observations and Environmental Parameters

Abstract Estimation of uncertainties (random error statistics) of radio occultation (RO) observations is important for their effective assimilation in numerical weather prediction (NWP) models. Average uncertainties can be estimated for large samples of RO observations and these statistics may be used for specifying the observation errors in NWP data assimilation. However, the uncertainties of individual RO observations vary, and so using average uncertainty estimates will overestimate the uncertainties of some observations and underestimate those of others, reducing their overall effectiveness in the assimilation. Several parameters associated with RO observations or their atmospheric environments have been proposed to estimate individual RO errors. These include the standard deviation of bending angle (BA) departures from either climatology in the upper stratosphere and lower mesosphere (STDV) or the sample mean between 40 and 60 km (STD4060), the local spectral width (LSW), and the magnitude of the horizontal gradient of refractivity (|∇HN|). In this paper we show how the uncertainties of two RO datasets, COSMIC-2 and Spire BA, as well as their combination, vary with these parameters. We find that the uncertainties are highly correlated with STDV and STD4060 in the stratosphere, and with LSW and |∇HN| in the lower troposphere. These results suggest a hybrid error model for individual BA observations that uses an average statistical model of RO errors modified by STDV or STD4060 above 30 km, and LSW or |∇HN| below 8 km. Significance Statement These results contribute to the understanding of the sources of uncertainties in radio occultation observations. They could be used to improve the effectiveness of these observations in their assimilation into numerical weather prediction and reanalysis models by improving the estimation of their observational errors.

Constraining cosmology with the Gaia-unWISE Quasar Catalog and CMB lensing: structure growth

We study the angular clustering of Quaia, a Gaia- and unWISE-based catalog of over a million quasars with an exceptionally well-defined selection function. With it, we derive cosmology constraints from the amplitude and growth of structure across cosmic time. We divide the sample into two redshift bins, centered at z = 1.0 and z = 2.1, and measure both overdensity auto-correlations and cross-correlations with maps of the Cosmic Microwave Background convergence measured by Planck. From these data, and including a prior from measurements of the baryon acoustic oscillations scale, we place constraints on the amplitude of the matter power spectrum σ 8 = 0.766 ± 0.034, and on the matter density parameter Ω m = 0.343+0.017 -0.019. These measurements are in reasonable agreement with Planck at the ∼ 1.4σ level, and are found to be robust with respect to observational and theoretical uncertainties. We find that our slightly lower value of σ 8 is driven by the higher-redshift sample, which favours a low amplitude of matter fluctuations. We present plausible arguments showing that this could be driven by contamination of the CMB lensing map by high-redshift extragalactic foregrounds, which should also affect other cross-correlations with tracers of large-scale structure beyond z ∼ 1.5. Our constraints are competitive with those from state-of-the-art 3×2-point analyses, but arise from a range of scales and redshifts that is highly complementary to those covered by cosmic shear data and most galaxy clustering samples. This, coupled with the unprecedented combination of volume and redshift precision achieved by Quaia, allows us to break the usual degeneracy between Ω m and σ 8.

Towards Safe Autonomous Driving: Decision Making with Observation-Robust Reinforcement Learning

Most real-world situations involve unavoidable measurement noises or perception errors which result in unsafe decision making or even casualty in autonomous driving. To address these issues and further improve safety, automated driving is required to be capable of handling perception uncertainties. Here, this paper presents an observation-robust reinforcement learning against observational uncertainties to realize safe decision making for autonomous vehicles. Specifically, an adversarial agent is trained online to generate optimal adversarial attacks on observations, which attempts to amplify the average variation distance on perturbed policies. In addition, an observation-robust actor-critic approach is developed to enable the agent to learn the optimal policies and ensure that the changes of the policies perturbed by optimal adversarial attacks remain within a certain bound. Lastly, the safe decision making scheme is evaluated on a lane change task under complex highway traffic scenarios. The results show that the developed approach can ensure autonomous driving performance, as well as the policy robustness against adversarial attacks on observations.

Hydraulic Bottom Friction and Aerodynamic Roughness Coefficients for Mangroves in Southwest Florida, USA

Mangroves are a natural feature that enhance the resilience of natural and built coastal environments worldwide. They mitigate the impacts of hurricanes by dissipating energy from storm surges and waves, as well as reducing wind speeds. To incorporate mangroves into storm surge simulations, surface roughness parameters that accurately capture mangrove effects are required. These effects are typically parameterized using Manning’s n bottom friction coefficient for overland flow and aerodynamic roughness length (z0) for wind speed reduction. This paper presents the suggested values for these surface roughness parameters based on field observation and a novel voxel-based processing method for laser scanning point clouds. The recommended Manning’s n and z0 values for mangroves in southwest Florida are 0.138 and 2.34 m, respectively. The data were also used to retrain a previously developed random forest model to predict these surface roughness parameters based on point cloud statistics. The addition of the mangrove sites to the training data produced mixed results, improving the predictions of z0 while weakening the predictions of Manning’s n. The paper concludes that machine learning models developed to predict environmental attributes using small datasets with predictor features containing subjective estimates are sensitive to the uncertainty in the field observations.

In situ or accreted? Using deep learning to infer the origin of extragalactic globular clusters from observables

ABSTRACT Globular clusters (GCs) are powerful tracers of the galaxy assembly process, and have already been used to obtain a detailed picture of the progenitors of the Milky Way (MW). Using the E-MOSAICS cosmological simulation of a (34.4 Mpc)3 volume that follows the formation and co-evolution of galaxies and their star cluster populations, we develop a method to link the origin of GCs to their observable properties. We capture this complex link using a supervised deep learning algorithm trained on the simulations, and predict the origin of individual GCs (whether they formed in the main progenitor or were accreted from satellites) based solely on extragalactic observables. An artificial neural network classifier trained on ∼50 000 GCs hosted by ∼700 simulated galaxies successfully predicts the origin of GCs in the test set with a mean accuracy of 89 per cent for the objects with $\rm [Fe/H]\lt -0.5$ that have unambiguous classifications. The network relies mostly on the alpha-element abundances, metallicities, projected positions, and projected angular momenta of the clusters to predict their origin. A real-world test using the known progenitor associations of the MW GCs achieves up to 90 per cent accuracy, and successfully identifies as accreted most of the GCs in the inner Galaxy associated to the Kraken progenitor, as well as all the Gaia-Enceladus GCs. We demonstrate that the model is robust to observational uncertainties, and develop a method to predict the classification accuracy across observed galaxies. The classifier can be optimized for available observables (e.g. to improve the accuracy by including GC ages), making it a valuable tool to reconstruct the assembly histories of galaxies in upcoming wide-field surveys.

The FLAMINGO project: revisiting the S8 tension and the role of baryonic physics

ABSTRACT A number of recent studies have found evidence for a tension between observations of large-scale structure (LSS) and the predictions of the standard model of cosmology with the cosmological parameters fit to the cosmic microwave background (CMB). The origin of this ‘S8 tension’ remains unclear, but possibilities include new physics beyond the standard model, unaccounted for systematic errors in the observational measurements and/or uncertainties in the role that baryons play. Here, we carefully examine the latter possibility using the new FLAMINGO suite of large-volume cosmological hydrodynamical simulations. We project the simulations onto observable harmonic space and compare with observational measurements of the power and cross-power spectra of cosmic shear, CMB lensing, and the thermal Sunyaev-Zel’dovich (tSZ) effect. We explore the dependence of the predictions on box size and resolution and cosmological parameters, including the neutrino mass, and the efficiency and nature of baryonic ‘feedback’. Despite the wide range of astrophysical behaviours simulated, we find that baryonic effects are not sufficiently large to remove the S8 tension. Consistent with recent studies, we find the CMB lensing power spectrum is in excellent agreement with the standard model, while the cosmic shear power spectrum, tSZ effect power spectrum, and the cross-spectra between shear, CMB lensing, and the tSZ effect are all in varying degrees of tension with the CMB-specified standard model. These results suggest that some mechanism is required to slow the growth of fluctuations at late times and/or on non-linear scales, but that it is unlikely that baryon physics is driving this modification.

Accurate and robust visual SLAM with a novel ray-to-ray line measurement model

Line feature is regarded as a more intuitive and accurate landmark than point feature in visual SLAM for its multiple-pixel comprehensiveness. However, uncertain factors, such as partial occlusion and noise, frequently hinder the mapping accuracy and destabilize the line-assisted SLAM system. Structural regulations and prior hypotheses are often used to tackle the issues, whereas only few people explore the impact of line feature optimization. In this paper, we attempt to improve the accuracy and robustness of visual SLAM system through line feature optimization process. First, a concise ray-to-ray residual model is proposed to replace the prevalent point-to-line model to integrally use line features. Second, the information matrix related to observation uncertainties is calculated to normalize the residual model, which aims to better balance the weights of different lines. Third, we add the line model to ORB-SLAM3 system and design the method of point-and-line based tracking and optimization. Finally, quantitative criteria are proposed to objectively evaluate the line feature map. Both synthetical and real datasets experiments are carried out to demonstrate the advantages of our algorithm in terms of camera ego-motion estimation and mapping. For camera ego-motion estimation experiments, the proposed ray-to-ray residual model produces more accurate results compared to state-of-the-art line-assisted SLAM/VIO algorithms. Furthermore, the model runs faster and obtains more robust results than the prevalent point-to-line reprojection residual model. For mapping experiments, quantitative criteria are proposed, which also open a new perspective to evaluate line-assisted SLAM systems, and give clues to evidence that the proposed method builds a more accurate line feature map.

Adaptive tracking control under quantized observations and observation uncertainty with unbounded variance

AbstractIn this paper, we present a matrix‐type adaptive tracking control scheme for a stochastic regression system with multi‐threshold quantized observations and observation uncertainty. This observation uncertainty is described by an additive random noise, whose variance could be a polynomial‐type rate of increase and tends to infinity. Our method, first, to handle simultaneously parameter uncertainty, quantized observation, and observation uncertainty, incorporates an online stochastic approximation‐type parameter identification algorithm. Second, based on the above identification algorithm, we construct a matrix‐type adaptive tracking control law by the certainty equivalence principle. In addition, under suitable conditions, the above identification algorithm and matrix‐type adaptive tracking control law in the mean square sense can ensure that the estimations of the unknown parameters converge to the true values and achieve asymptotically optimal tracking of the periodic reference signal, respectively. To characterize the effect of observation uncertainty on the convergence speed of the identification algorithm in the mean square sense, a quantitative relationship between observation uncertainty and the convergence speed is proposed. Finally, the effectiveness of the proposed matrix‐type adaptive tracking control scheme is verified through a simulation.

Emergent constraints on future extreme precipitation intensification: from global to continental scales

Extreme precipitation intensification due to global warming has received significant attention; however, large uncertainties remain regarding how much it will change in the future, even under a given greenhouse gas emissions pathway. Our constraining analysis provides smaller future extreme precipitation intensification with reduced uncertainties than raw/unconstrained model simulation projections from the global to continental scales and also several sub-continental areas. Historical warming trends in climate model simulations present strong positive inter-model correlations with future annual maximum daily precipitation intensification from the global to continental scales. This emergent relationship is employed for constraining analysis. The proposed emergent constraints are mostly associated with thermodynamics (atmospheric moisture changes), with limited contribution from dynamics (atmospheric circulation variations). This constraining framework has various advantages, including lower observation uncertainty, and more robust theoretical interpretation than previously suggested constraints for extreme precipitation frequency projections. CMIP6 models generally overestimate historically observed warming, which resultantly generates weaker extreme precipitation intensification following constraining analysis than unconstrained model simulation projections from the global to individual continents, and even several mid- and high-latitude sub-continental areas. Additionally, constrained projections exhibit narrower uncertainty ranges in future extreme precipitation projections. During the late 21st century (2070–2099), under the high-emission scenario (Shared Socioeconomic Pathway 5–8.5, characterized by fossil fueled development and a radiative forcing of 8.5 W m−2 in 2100), our results present reduced global average intensity and uncertainty for future annual maximum daily precipitation intensification by 25% and 27%, respectively.

Effects of multi-observations uncertainty and models similarity on climate change projections

Climate change projections (CCPs) are based on the multimodel means of individual climate model simulations that are assumed to be independent. However, model similarity leads to projections biased toward the largest set of similar models and intermodel uncertainty underestimation. We assessed the influences of similarities in CMIP6 through CMIP3 CCPs. We ascertained model similarity from shared physics/dynamics and initial conditions by comparing simulated spatial temperature and precipitation with the corresponding observed patterns and accounting for intermodel spread relative to the observational uncertainty, which is also critical. After accounting for similarity, the information from 57 CMIP6, 47 CMIP5, and 24 CMIP3 models can be explained by just 11 independent models without significant differences in globally averaged climate change statistics. On average, independent models indicate a lower global-mean temperature rise of 0.25 °C (~0.5 °C–1 °C in some regions) relative to all models by the end of the 21st century under CMIP6’s highest emission scenario.

Robust Multiagent Reinforcement Learning toward Coordinated Decision-Making of Automated Vehicles

<div>Automated driving is essential for developing and deploying intelligent transportation systems. However, unavoidable sensor noises or perception errors may cause an automated vehicle to adopt suboptimal driving policies or even lead to catastrophic failures. Additionally, the automated driving longitudinal and lateral decision-making behaviors (e.g., driving speed and lane changing decisions) are coupled, that is, when one of them is perturbed by unknown external disturbances, it causes changes or even performance degradation in the other. The presence of both challenges significantly curtails the potential of automated driving. Here, to coordinate the longitudinal and lateral driving decisions of an automated vehicle while ensuring policy robustness against observational uncertainties, we propose a novel robust coordinated decision-making technique via robust multiagent reinforcement learning. Specifically, the automated driving longitudinal and lateral decisions under observational perturbations are modeled as a constrained robust multiagent Markov decision process. Meanwhile, a nonlinear constraint setting with Kullback–Leibler divergence is developed to keep the variation of the driving policy perturbed by stochastic perturbations within bounds. Additionally, a robust multiagent policy optimization approach is proposed to approximate the optimal robust coordinated driving policy. Finally, we evaluate the proposed robust coordinated decision-making method in three highway scenarios with different traffic densities. Quantitatively, in the absence of noises, the proposed method achieves an approximate average enhancement of 25.58% in traffic efficiency and 91.31% in safety compared to all baselines across the three scenarios. In the presence of noises, our technique improves traffic efficiency and safety by an approximate average of 30.81% and 81.02% compared to all baselines in the three scenarios, respectively. The results demonstrate that the proposed approach is capable of improving automated driving performance and ensuring policy robustness against observational uncertainties.</div>