Large Outliers Research Articles

A robust iterative algorithm for SINS/USBL integrated navigation based on dual hydrophone differential model

Aiming at the complex underwater environment which leads to large measurement errors and outliers in ultrashort baseline, a robust iterative Kalman filtering algorithm based on the differential model of dual hydrophones is proposed in this paper. Firstly, to achieve accurate navigation under large initial misalignment angles, the algorithm builds an inertial navigation error equation based on the right invariant error definition of Lie groups. Then a dual hydrophone differential model is proposed based on SINS/USBL tightly coupled. It successfully improves the accuracy of integrated navigation and significantly mitigating the issue of SINS/USBL positioning accuracy degradation caused by USBL slant distance noise that grows with distance. Finally, a robust iterative Kalman filter based on the dual hydrophone differential model is proposed to counter the threat to the vehicle’s safe operation and the accuracy loss caused by outliers. The algorithm’s superiority and effectiveness are verified through simulation and sea trial.

Robust Low-Tubal-Rank Tensor Completion Based on Tensor Factorization and Maximum Correntopy Criterion.

The goal of tensor completion is to recover a tensor from a subset of its entries, often by exploiting its low-rank property. Among several useful definitions of tensor rank, the low tubal rank was shown to give a valuable characterization of the inherent low-rank structure of a tensor. While some low-tubal-rank tensor completion algorithms with favorable performance have been recently proposed, these algorithms utilize second-order statistics to measure the error residual, which may not work well when the observed entries contain large outliers. In this article, we propose a new objective function for low-tubal-rank tensor completion, which uses correntropy as the error measure to mitigate the effect of the outliers. To efficiently optimize the proposed objective, we leverage a half-quadratic minimization technique whereby the optimization is transformed to a weighted low-tubal-rank tensor factorization problem. Subsequently, we propose two simple and efficient algorithms to obtain the solution and provide their convergence and complexity analysis. Numerical results using both synthetic and real data demonstrate the robust and superior performance of the proposed algorithms.

The Impact of Indication for Cesarean on Blood Loss

BackgroundPostpartum hemorrhage is the leading cause of maternal mortality worldwide. Quantitative blood loss assessment at the time of cesarean delivery is a more accurate measure of blood loss than is simple estimation. Risk factors for postpartum hemorrhage are well described; however, contemporary, systematic investigations into the impact of the indication for cesarean delivery on quantitative blood loss are lacking. ObjectiveTo investigate whether there are clinically significant differences in quantitative blood loss and postpartum hemorrhage risk based on the indication for cesarean delivery. Study DesignA cohort of 4,881 cesarean deliveries performed at a large academic hospital between 2020 and 2022 was identified. Primary and repeat cesarean deliveries were analyzed separately and further subdivided into seven indications: elective, labor arrest, fetal heart rate abnormalities, placenta previa, placenta accreta, malpresentation, and other. Quantitative blood loss and rates of postpartum hemorrhage ( > 1000 cc and >1500cc) were compared among the different indications. ResultsMean quantitative blood loss for primary, repeat and total cesarean deliveries was 886 cc, 697 cc and 792 cc, respectively. Excluding cases of placenta accreta, the greatest blood loss in both primary and repeat groups was seen in cesareans performed for labor arrest, with blood loss exceeding 1500 cc in 18% and 13% of all cases. Blood loss exceeding 1500cc was seen in 1% and 2% of elective cesarean deliveries. The mean blood loss for planned repeat cesarean/hysterectomies for placenta accreta was < 400cc greater than primary cesareans performed for labor arrest (1442 cc vs. 1065 cc), despite the addition of an often-complex hysterectomy to the procedure. ConclusionsClinically and statistically significant differences in blood loss exist based on the indication for cesarean delivery. Large differences in rates of serious postpartum hemorrhage (>1500cc) with negligible differences in mean quantitative blood loss suggests the presence of frequent, large clinical outliers not reflected in a statistical mean. The indication for cesarean and the possibility of such outliers, rather than the predicted “average blood loss for cesarean delivery” should be considered in determining risk and the degree of necessary pre-operative blood preparation. These data raise questions about whether current, traditional techniques of non-accreta related cesarean delivery can be justified in non-emergent cases when such procedures could be performed with significantly less blood loss using accreta-specific techniques.

Efficient and Robust Point Cloud Registration via Heuristics-Guided Parameter Search.

Estimating the rigid transformation with 6 degrees of freedom based on a putative 3D correspondence set is a crucial procedure in point cloud registration. Existing correspondence identification methods usually lead to large outlier ratios (>95% is common), underscoring the significance of robust registration methods. Many researchers turn to parameter search-based strategies (e.g., Branch-and-Bround) for robust registration. Although related methods show high robustness, their efficiency is limited to the high-dimensional search space. This paper proposes a heuristics-guided parameter search strategy to accelerate the search while maintaining high robustness. We first sample some correspondences (i.e., heuristics) and then just need to sequentially search the feasible regions that make each sample an inlier. Our strategy largely reduces the search space and can guarantee accuracy with only a few inlier samples, therefore enjoying an excellent trade-off between efficiency and robustness. Since directly parameterizing the 6-dimensional nonlinear feasible region for efficient search is intractable, we construct a three-stage decomposition pipeline to reparameterize the feasible region, resulting in three lower-dimensional sub-problems that are easily solvable via our strategy. Besides reducing the searching dimension, our decomposition enables the leverage of 1-dimensional interval stabbing at all three stages for searching acceleration. Moreover, we propose a valid sampling strategy to guarantee our sampling effectiveness, and a compatibility verification setup to further accelerate our search. Extensive experiments on both simulated and real-world datasets demonstrate that our approach exhibits comparable robustness with state-of-the-art methods while achieving a significant efficiency boost.

Implicit Is Not Enough: Explicitly Enforcing Anatomical Priors inside Landmark Localization Models.

The task of localizing distinct anatomical structures in medical image data is an essential prerequisite for several medical applications, such as treatment planning in orthodontics, bone-age estimation, or initialization of segmentation methods in automated image analysis tools. Currently, Anatomical Landmark Localization (ALL) is mainly solved by deep-learning methods, which cannot guarantee robust ALL predictions; there may always be outlier predictions that are far from their ground truth locations due to out-of-distribution inputs. However, these localization outliers are detrimental to the performance of subsequent medical applications that rely on ALL results. The current ALL literature relies heavily on implicit anatomical constraints built into the loss function and network architecture to reduce the risk of anatomically infeasible predictions. However, we argue that in medical imaging, where images are generally acquired in a controlled environment, we should use stronger explicit anatomical constraints to reduce the number of outliers as much as possible. Therefore, we propose the end-to-end trainable Global Anatomical Feasibility Filter and Analysis (GAFFA) method, which uses prior anatomical knowledge estimated from data to explicitly enforce anatomical constraints. GAFFA refines the initial localization results of a U-Net by approximately solving a Markov Random Field (MRF) with a single iteration of the sum-product algorithm in a differentiable manner. Our experiments demonstrate that GAFFA outperforms all other landmark refinement methods investigated in our framework. Moreover, we show that GAFFA is more robust to large outliers than state-of-the-art methods on the studied X-ray hand dataset. We further motivate this claim by visualizing the anatomical constraints used in GAFFA as spatial energy heatmaps, which allowed us to find an annotation error in the hand dataset not previously discussed in the literature.

In DFT We Trust: Exhaustive Exploration of 1,3‐Dipolar Cycloadditions Between Nitrones and Levoglucosenone Exposes a Curious Case of Conformational Dynamics

AbstractAn experimental and computational study (including DFT calculations and distortion/interaction analysis) was conducted to assess the effect of the nitrone structure in the outcome of dipolar 1,3‐cycloadditions with levoglucosenone, a biomass derived chiral enone. While B3LYP/6‐31G* (the most popular method for modeling these reactions according to our literature search) provides qualitatively good results, large outliers were found for some systems versus experimental data. An exhaustive exploration of other levels allowed us to determine the most appropriate ones to predict simultaneously reactivity and selectivity. The systematically predicted high exo selectivity by the majority of the levels led us to reconsider our initial assignment for the reaction with the nitrone derived from piperidine, which resulted in the discovery of an interesting case of conformational dynamics.

Except for Robust Outliers, Rapamycin Increases Lesion Burden in a Murine Model of Cerebral Cavernous Malformations.

Cerebral cavernous malformation (CCM) is a hemorrhagic cerebrovascular disease where lesions develop in the setting of endothelial mutations of CCM genes, with many cases also harboring somatic PIK3CA gain of function (GOF) mutations. Rapamycin, an mTORC1 inhibitor, inhibited progression of murine CCM lesions driven by Ccm gene loss and Pik3ca GOF, but it remains unknown if rapamycin is beneficial in the absence of induction of Pik3ca GOF. We investigated the effect of rapamycin at three clinically relevant doses on lesion development in the Ccm3-/-PDGFb-icreERPositive murine model of familial CCM disease, without induction of Pik3ca GOF. Lesion burden, attrition, and acute and chronic hemorrhaging were compared between placebo and rapamycin-treated mice. Plasma miRNome was compared to identify potential biomarkers of rapamycin response. Outlier, exceptionally large CCM lesions (> 2 SD above the mean lesion burden) were exclusively observed in the placebo group. Rapamycin, across all dosages, may have prevented the emergence of large outlier lesions. Yet rapamycin also appeared to exacerbate mean lesion burden of surviving mice when outliers were excluded, increased attrition, and did not alter hemorrhage. miR-30c-2-3p, decreased in rapamycin-treated mouse plasma, has gene targets in PI3K/AKT and mTOR signaling. Progression of outlier lesions in a familial CCM model may have been halted by rapamycin treatment, at the potential expense of increased mean lesion burden and increased attrition. If confirmed, this can have implications for potential rapamycin treatment of familial CCM disease, where lesion development may not be driven by PIK3CA GOF. Further studies are necessary to determine specific pathways that mediate potential beneficial and detrimental effects of rapamycin treatment, and whether somatic PIK3CA mutations drive particularly aggressive lesions.

Condition monitoring based on corrupted multiple time series with common trends

Condition monitoring is a fundamental task in the reliability engineering and operation management of a complex industrial system. It aims to detect faults based on sensing data but poses significant challenges when dealing with corrupted multiple time series data in many real-world applications. These time series typically exhibit similar changing patterns influenced by common trends (e.g., workload, ambient condition) and physical relationships among corresponding variables, and are often significantly corrupted large outliers (e.g., transmission interruption). Although several traditional common trend models have been employed to analyze such condition monitoring data in the literature, they are fully parametric, constrained by restrictive assumptions, and not robust to outliers. In this article, we propose a novel semiparametric decomposition model to analyze a set of monitored time series and separate it into common, idiosyncratic, and sparse components, under relatively mild assumptions. We also introduce effective algorithms for model estimation and a monitoring scheme for fault detection. The numerical and real case studies demonstrate the superiority of the proposed method over existing approaches, in terms of both decomposition accuracy and detection performance for system faults.

Estimating Surgical Urethral Length on Intraoperative Robot-Assisted Prostatectomy Images Using Artificial Intelligence Anatomy Recognition.

Objective: To construct a convolutional neural network (CNN) model that can recognize and delineate anatomic structures on intraoperative video frames of robot-assisted radical prostatectomy (RARP) and to use these annotations to predict the surgical urethral length (SUL). Background: Urethral dissection during RARP impacts patient urinary incontinence (UI) outcomes, and requires extensive training. Large differences exist between incontinence outcomes of different urologists and hospitals. Also, surgeon experience and education are critical toward optimal outcomes. Therefore, new approaches are warranted. SUL is associated with UI. Artificial intelligence (AI) surgical image segmentation using a CNN could automate SUL estimation and contribute toward future AI-assisted RARP and surgeon guidance. Methods: Eighty-eight intraoperative RARP videos between June 2009 and September 2014 were collected from a single center. Two hundred sixty-four frames were annotated according to prostate, urethra, ligated plexus, and catheter. Thirty annotated images from different RARP videos were used as a test data set. The dice (similarity) coefficient (DSC) and 95th percentile Hausdorff distance (Hd95) were used to determine model performance. SUL was calculated using the catheter as a reference. Results: The DSC of the best performing model were 0.735 and 0.755 for the catheter and urethra classes, respectively, with a Hd95 of 29.27 and 72.62, respectively. The model performed moderately on the ligated plexus and prostate. The predicted SUL showed a mean difference of 0.64 to 1.86 mm difference vs human annotators, but with significant deviation (standard deviation = 3.28-3.56). Conclusion: This study shows that an AI image segmentation model can predict vital structures during RARP urethral dissection with moderate to fair accuracy. SUL estimation derived from it showed large deviations and outliers when compared with human annotators, but with a small mean difference (<2 mm). This is a promising development for further research on AI-assisted RARP.

Militarized granularity: Sand’s making of men and masculinity in Singapore

ABSTRACT Land reclamation is the process of transforming mined sand from rivers, estuaries, the seabed and even islands into new land parcels. This article examines Singapore’s insatiable appetite for sand and discusses the relationality between a singular grain of sand’s capacity to militarize and gender the Southeast Asian island-state. With opaque sand supply chains, Singapore can project its sovereignty as a militarized nation-state through the neo-colonialist like extraction of sand from other countries to widen its territorial frontiers. In placing the spotlight on extensive land-reclamation works to expand Tekong Island, Singapore’s largest outlier into a military base, I explain how sand serves the role of creating a militarized boundary object. While sand’s coarseness configures military masculinity, sand’s smoothness serves the additional function of maintaining civilian support for militarism by way of ‘packaging’ masculinity. The granular cartographies of trauma arising from Singapore’s territorial expansions wrought upon Southeast Asia’s rural poor require urgent recuperation.

Applicability of Oculomics for Individual Risk Prediction: Repeatability and Robustness of Retinal Fractal Dimension Using DART and AutoMorph.

To investigate whether fractal dimension (FD)-based oculomics could be used for individual risk prediction by evaluating repeatability and robustness. We used two datasets: "Caledonia," healthy adults imaged multiple times in quick succession for research (26 subjects, 39 eyes, 377 color fundus images), and GRAPE, glaucoma patients with baseline and follow-up visits (106 subjects, 196 eyes, 392 images). Mean follow-up time was 18.3 months in GRAPE; thus it provides a pessimistic lower bound because vasculature could change. FD was computed with DART and AutoMorph. Image quality was assessed with QuickQual, but no images were initially excluded. Pearson, Spearman, and intraclass correlation (ICC) were used for population-level repeatability. For individual-level repeatability, we introduce measurement noise parameter λ, which is within-eye standard deviation (SD) of FD measurements in units of between-eyes SD. In Caledonia, ICC was 0.8153 for DART and 0.5779 for AutoMorph, Pearson/Spearman correlation (first and last image) 0.7857/0.7824 for DART, and 0.3933/0.6253 for AutoMorph. In GRAPE, Pearson/Spearman correlation (first and next visit) was 0.7479/0.7474 for DART, and 0.7109/0.7208 for AutoMorph (all P < 0.0001). Median λ in Caledonia without exclusions was 3.55% for DART and 12.65% for AutoMorph and improved to up to 1.67% and 6.64% with quality-based exclusions, respectively. Quality exclusions primarily mitigated large outliers. Worst quality in an eye correlated strongly with λ (Pearson 0.5350-0.7550, depending on dataset and method, all P < 0.0001). Repeatability was sufficient for individual-level predictions in heterogeneous populations. DART performed better on all metrics and might be able to detect small, longitudinal changes, highlighting the potential of robust methods.

Verification of a nationwide population-based myopia growth chart in a large longitudinal cohort of 1155 Korean children.

To evaluate predictive value of the myopia growth chart based on population-based health survey data using longitudinal cohorts. Patients aged from 5 to 18years and underwent two or more cycloplegic refraction (CR) exams with at least one year of interval were included. Percentile deviation was calculated by subtracting percentile at final exam from the percentile at initial exam based on the chart. Spherical equivalent (SE) deviation was calculated by SE at final CR subtracted from predicted SE based on initial CR using the chart. 2310 eyes from 1155 subjects were included. There were 1344 eyes (58.2%) categorized as inliers, where both initial and final CR were within the 2nd to 99th percentile. Mean percentile and SE deviations were + 11.0 ± 22.9 percentiles and -0.60 ± 1.33 diopters, each. Outliers, those except the inliers, were 966 eyes (41.8%). Most outliers (709 eyes, 73.4%) were outside the chart for both initial and final exam. The rest of the outliers (257 eyes, 26.2%) were within the 2 to 99 percentile range on the chart at least once, either at initial or final exams; most of those (202 eyes, 78.6%) progressed toward myopia more than predicted. In our large cohorts, both inliers and outliers tended to progress toward more myopia than predicted from the chart. This suggests the chart predicts childhood myopia rather conservatively. The myopia growth chart may be useful as a screening tool in detecting children at high risk of developing high myopia.

Robust sparse concept factorization with graph regularization for subspace learning

Concept factorization (CF) is a powerful tool in subspace learning. Recently graph-based CF and local coordinate CF have been proposed to exploit the intrinsic geometrical structure of data, and have been shown quite successful in improving performance. However, these methods have limited robustness and might be sensitive to noises and disturbances in practical applications. In this paper, we propose a novel robust sparse CF framework (RSCF) for subspace learning. Specifically, we present a robust loss function to effectively eliminate the impact of the large outliers. The local coordinate constraint and the graph regularization term are incorporated into RSCF to simultaneously guarantee the sparsity of the coefficient matrix and maintain the local structure of the data. We prove that the local coordinate constraint implies the orthogonality of the coefficient matrix. By using the half-quadratic optimization technique, we transform the objective function of RSCF into a quadratic form and provide the iterative updating rules and the convergence analysis. Extensive experiments demonstrate the robustness and superiority of the proposed RSCF in comparison to state-of-the-art CF methods.

Robust multimodulus blind equalization algorithm for multilevel QAM signals in impulsive noise

As a blind equalization algorithm, the fractional lower-order statistics based multimodulus algorithm (FLOS-MMA) is suitable for equalizing multilevel quadrature amplitude modulation (QAM) signals in impulsive noise. However, the equalization performance of FLOS-MMA is severely degraded in strong impulsive noise because the first-order gradient of the cost function constrains the ability of FLOS to suppress large outliers in the equalizer output signal and is sensitive to large outliers in the equalizer input signal. To enhance the robustness of multimodulus blind equalization algorithm against impulsive noise, a FLOS based momentum fractional-order multimodulus algorithm (FLOS-MFOMMA) is proposed in this paper. The proposed algorithm mitigates the adverse effects of impulsive noise on the updating of equalizer coefficients by utilizing the fractional-order gradient and accelerates the convergence speed by incorporating a momentum term. Then, the steady-state excess mean square error and the computational complexity of FLOS-MFOMMA are analyzed theoretically. Finally, numerical simulations, for equalizing 16-QAM signals under impulsive noise environments, show that the proposed algorithm is superior to FLOS-MMA in terms of robustness and convergence speed.

TRigon: an R package and Shiny App for integrative (path-)omics data analysis

BackgroundPathomics facilitates automated, reproducible and precise histopathology analysis and morphological phenotyping. Similar to molecular omics, pathomics datasets are high-dimensional, but also face large outlier variability and inherent data missingness, making quick and comprehensible data analysis challenging. To facilitate pathomics data analysis and interpretation as well as support a broad implementation we developed tRigon (Toolbox foR InteGrative (path-)Omics data aNalysis), a Shiny application for fast, comprehensive and reproducible pathomics analysis.ResultstRigon is available via the CRAN repository (https://cran.r-project.org/web/packages/tRigon) with its source code available on GitLab (https://git-ce.rwth-aachen.de/labooratory-ai/trigon). The tRigon package can be installed locally and its application can be executed from the R console via the command ‘tRigon::run_tRigon()’. Alternatively, the application is hosted online and can be accessed at https://labooratory.shinyapps.io/tRigon. We show fast computation of small, medium and large datasets in a low- and high-performance hardware setting, indicating broad applicability of tRigon.ConclusionstRigon allows researchers without coding abilities to perform exploratory feature analyses of pathomics and non-pathomics datasets on their own using a variety of hardware.

Forecasting geomagnetic activity: Neural networks, moving windows and state transition models

Geomagnetic activity shows high degrees of nonlinear variability. The probability distribution has heavy tails, and there are intermittent outliers. This has led to increased interest in forecasting using neural networks and nonlinear regressions, which include time-varying coefficient techniques. Because geomagnetic storms pose the greatest threat to satellites and power grids, there is a particular interest in predicting outlying events. The model proposed here combines two techniques. Neural networks and regressions are trained over moving windows of observations, so that the weights or coefficients adjust to new data. Second, logistic regression is used to predict the periods of high activity, and the cumulative distribution function is used as a causal input in time series and machine learning models. The data set is the Aa index, corrected for secular drift. Forecasting experiments are run over horizons of 1–4 days. The other models include time-varying parameter regressions and a recurrent neural network with fixed weights. The model combining the neural net and logistic regression achieves the most accurate forecast, although the regression is a close second. The ability to predict outliers depends on the width of the moving window. With wider windows, the overall error is lower, but the forecasted values fall into a narrower range, missing the outliers. With narrower windows, the model predicts the outliers better but is vulnerable to calling them at the wrong times, so the average error is higher. Further, while the model achieves more accurate predictions at 1 day, at longer horizons the accuracy deteriorates quite rapidly.

Novel iterative cubature Kalman filters under maximum correntropy criterion for the robust state estimation

AbstractThe cubature Kalman filter (CKF) based on maximum correntropy criterion (MCC) is robust under non‐Gaussian noises, but it may encounter numerical problems when there are large outliers. First, based on the MCC and weighted least square (WLS) methods, a new cost function is introduced to calculate the state and covariance updates, which can better solve numerical problems caused by large outliers. Then, a nonlinear measurement function is used, and the measurement information is updated through the latest iterative values, which can obtain more accurate results under various non‐Gaussian noises. In addition, the Gauss–Newton, Levenberg–Marquardt, and Quasi–Newton methods are used, and three iterative algorithms named GN‐IMCC‐CKF, LM‐IMCC‐CKF, and QN‐IMCC‐CKF are respectively derived for the updating steps. Compared with the existing algorithms, the simulation results of two classical models show that the proposed algorithms have better numerical stability and estimation accuracy under non‐Gaussian systems.

Deep Correspondence Matching-Based Robust Point Cloud Registration of Profiled Parts

Due to ability to estimate the spatial transformation of coordinate frames, point cloud registration is a fundamental technique in manufacturing. Previous methods prone to converge to wrong local minima, in the cases of large initialization, noise, outliers, and partiality. This study presents a new learning-based robust point cloud registration approach to predict a rigid transformation in a one-shot way. Our network aims to determine a matchability matrix to yield an accurate registration result. Each element of the matchability matrix refers to similarity of learned per-point embeddings and represents the probability of a potential correspondence. Two major blocks are developed to guide the matchability matrix to represent correct correspondences: 1) an attention block is introduced to enhance the discriminativeness of learned per-point embeddings; 2) a zero-mean Gaussian based annealing layer and a differentiable Sinkhorn normalization layer are designed to enforce a permutation matchability matrix. With the matchability matrix, an intuitive solution is integrated to obtain the relative transformation of the source and target point clouds. Different from existing work, our network can handle partially overlapped point-cloud pairs effectively. Experimental results demonstrate the superiority of the proposed approach over the state-of-the-art registration approaches in terms of accuracy and robustness.

Performance of periodic EOM-CCSD for bandgaps of inorganic semiconductors and insulators.

We calculate bandgaps of 12 inorganic semiconductors and insulators composed of atoms from the first three rows of the Periodic Table using periodic equation-of-motion coupled-cluster theory with single and double excitations (EOM-CCSD). Our calculations are performed with atom-centered triple-zeta basis sets and up to 64 k-points in the Brillouin zone. We analyze the convergence behavior with respect to the number of orbitals and number of k-points sampled using composite corrections and extrapolations to produce our final values. When accounting for electron-phonon corrections to experimental bandgaps, we find that EOM-CCSD has a mean signed error of -0.12eV and a mean absolute error of 0.42eV; the largest outliers are C (error of -0.93eV), BP (-1.00eV), and LiH (+0.78eV). Surprisingly, we find that the more affordable partitioned EOM-MP2 theory performs as well as EOM-CCSD.

A meticulous covariance adaptive Kalman filter for satellite attitude estimation

Aiming at the problems of model errors, non-Gaussian noise and measurement anomaly in the spacecraft attitude estimation system, this article proposes an improved adaptive filtering method based on covariance matching, which solves the problems of simultaneous dynamics model error and measurement model error in the attitude estimation system, and at the same time, effectively reduces the effects of non-Gaussian noise and large outlier situations occurring in the vector measurement sensor. Firstly, an adaptive filtering algorithm based on the innovation sequence estimation covariance is investigated under the framework of multiplicative extended Kalman filter (MEKF), which is used to correct process noise covariance, then the Sage–Husa adaptive Kalman filtering (SHAKF) method is combined to correct the measurement noise covariance, and finally the meticulous covariance adaptive multiplicative extended Kalman filter is designed. The proposed algorithm uses both innovation and SHAKF methods to correct the two covariance matrices simultaneously. Several attitude estimation simulation scenarios are set up to simulate the proposed algorithm in the presence of model errors, non-Gaussian noise, and large outlier. The simulation results demonstrate that the proposed algorithm outperforms the conventional algorithms in terms of estimation accuracy and robustness.