Inverse Estimation Research Articles

The role of cardiovascular health and vascular events in the relationship between excessive daytime sleepiness and dementia risk.

Many studies suggest a relationship between excessive daytime sleepiness (EDS) and dementia incidence, but the underlying mechanisms remain uncertain. The study aimed to investigate the role of cardiovascular burden in the relationship between EDS and dementia incidence over a 12-year follow-up in community-dwelling older adults. We performed analyses on 6171 subjects (aged ≥65 years) free of dementia and vascular disease at baseline. Participants self-reported EDS at baseline and an expert committee validated both prevalent and incident dementia. We defined cardiovascular burden by a low Cardiovascular Health score, constructed using the American Heart Association metrics, and incident vascular events. To explore the potential role of the cardiovascular burden in the relationship between EDS and dementia, we conducted mediation analyses with inverse odds ratio-weighted estimation, using multivariable-adjusted proportional hazard Cox and logistic regression models. Subjects with EDS had a higher risk of all-cause dementia (hazard ratio [HR] 1.39, 95% confidence interval [CI] 1.13-1.69) and dementia with vascular component (DVC) (HR 2.14, 95% CI 1.30-3.51), but not Alzheimer's disease (HR 1.18, 95% CI 0.93-1.51). Cardiovascular burden explained 5% (95% CI 4.1-5.2) and 11% (95% CI 9.7-11.3) of the relationship between EDS and all-cause dementia and DVC, respectively. These findings confirm that EDS may be implicated in the development of dementia and indicate a weaker than expected role of cardiovascular burden in the relationship between EDS and DVC.

Conditional Karhunen–Loève regression model with Basis Adaptation for high-dimensional problems: Uncertainty quantification and inverse modeling

We propose a methodology for improving the accuracy of surrogate models of the observable response of physical systems as a function of the systems’ spatially heterogeneous parameter fields, with applications to uncertainty quantification and parameter estimation in high-dimensional problems. Practitioners often formulate finite-dimensional representations of spatially heterogeneous parameter fields using truncated unconditional Karhunen–Loève expansions (KLEs) for a certain choice of unconditional covariance kernel and construct surrogate models of the observable response with respect to the KLE coefficients. When direct measurements of the parameter fields are available, we propose improving the accuracy of these surrogate models by representing the parameter fields via conditional Karhunen-Loève expansions (CKLEs). CKLEs are constructed by conditioning the covariance kernel of the unconditional expansion on the direct measurements of the parameter field via Gaussian process regression, and then truncating the corresponding KLE.We apply the proposed methodology to constructing surrogate models via the Basis Adaptation (BA) method of the stationary hydraulic head response, measured at spatially discrete observation locations, of a groundwater flow model of the Hanford Site, as a function of the 1000-dimensional representation of the model’s log-transmissivity field. We find that BA surrogate models of the hydraulic head based on CKLEs are more accurate than BA surrogate models based on unconditional expansions for forward uncertainty quantification tasks. Furthermore, we find that inverse estimates of the hydraulic transmissivity field computed using CKLE-based BA surrogate models are more accurate than those computed using unconditional BA surrogate models.

The Rapid Detection of Trash Content in Seed Cotton Using Near-Infrared Spectroscopy Combined with Characteristic Wavelength Selection

Herein, we propose a new method based on Fourier-transform near-infrared spectroscopy (FT-NIR) for detecting impurities in seed cotton. Based on the spectral data of 152 seed cotton samples, we screened the characteristic wavelengths in full-band spectral data with regard to potential correlation with the trash content of seed cotton. Then, we applied joint synergy interval partial least squares (siPLS) and combinatory algorithms with the competitive adaptive reweighted sampling method (CARS) and the successive projection algorithm (SPA). In addition, we used the sparrow search algorithm (SSA), gray wolf algorithm (GWO), and eagle algorithm (BES) to optimize parameters for support vector machine (SVM) analysis. Finally, the feature wavelengths optimized via the six feature wavelength extraction algorithms were modeled and analyzed via partial least squares (PLS), SSA-SVM, GWO-SVM, and BES-SVM, respectively. The correlation coefficients, Rc and Rp, of the calibration and prediction sets were subsequently used as model evaluation indices; comparative analysis highlighted that the preferred option was the inverse estimation model as this could accurately predict the trash content of seed cotton. Subsequently, we found that the accuracy of predicting the content of impurities in seed cotton when applying the optimized SVM model of SSA combined with the feature wavelengths screened via siPLS-SPA was optimal. Thus, the optimal modeling method for inverse impurity content was siPLS-SPA-SSA-SVM, with an Rc value of 0.9841 and an Rp value of 0.9765. The rapid application development (RPD) value was 6.7224; this is >3, indicating excellent predictive ability. The spectral inversion model for determining the impurity rate of mechanized harvested seed cotton samples established herein can, therefore, determine the impurity rate in a highly accurate manner, thus providing a reference for the subsequent construction of a portable spectral detector of impurity rate. This will help objectively and quantitatively characterize the impurity rate of mechanized harvested seed cotton and provide a new tool for rapidly detecting impurities in mechanized harvested wheat. Our findings are limited by the small sample size and the fact that the model developed for estimating the impurity content of seed cotton was specific to a local experimental field and certain varieties of cotton.

Geometric inverse estimation for the inner wall of a furnace under transient heat conduction based on dual reciprocity boundary element method

A boundary shape estimation problem for the furnace’s inner surface is solved using the dual reciprocity boundary element method (DRBEM) and the conjugate gradient method (CGM) under transient heat conduction. The DRBEM is utilized to eliminate the drawbacks of traditional numerical methods and classical boundary element method of discretizing the entire computational domain, with only boundary discretization. The inversion results are obtained by applying the CGM to minimize the objective function, in which the sensitivity coefficients are calculated with the complex variable derivation method (CVDM), making the calculation precise and independent of step size. To verify the accuracy of the DRBEM in solving the transient heat conduction problem, the influencing factors including radial-basis function, the number of internal collocation points, and time step size are investigated. The influences of measurement time interval, future time step, initial guess, measurement error, and the number and position of measurement points on the inversion results are analyzed. Meanwhile, the effectiveness of the proposed approach is tested by numerical examples, and the inversion results show that it is stable, accurate, and efficient for identifying different and complicated unknown boundary shapes of the furnace.

Enhancing scientific transparency in national CO2 emissions reports via satellite-based a posteriori estimates

Biennial Update Reports (BURs) are essential requirements from the United Nations Framework Convention on Climate Change (UNFCCC). However, many non-Annex I countries have not submitted these reports due to difficulties in compiling the inventories. We developed a satellite-based method for the top-down inverse estimation of CO2 emissions using partial-column data in the lower troposphere obtained by the Greenhouse Gases Observing Satellite, adopted to validate the Mongolian 2nd BUR (BUR2) for the energy sector in 2018. The estimated CO2 emissions were only 1.5% higher than those reported in the BUR2; these were also very close (4.2% smaller) to estimates from the Emission Database for Global Atmospheric Research. Mongolia is the first country to introduce an independent inverse estimate in its BUR, thereby increasing scientific transparency. Our method could be applied into other countries and could be incorporated into UNFCCC reporting guidelines, significantly improving global CO2 emission estimates.

Clean energy use and subjective and objective health outcomes in rural China

This study analyses the impact of clean energy use on rural residents' subjective health outcomes (self-reported health status, health change, and discomfort) and objective health outcomes (bronchitis, asthma, medical expenditure, and fitness expenditure). Using an inverse probability-weighted regression adjustment estimator and the 2018 China Family Panel Studies data, we address the selection bias associated with clean energy use and estimate the treatment effects. The empirical results show that farmers using clean energy (liquid gas, natural gas, methane, solar energy, or electricity) as the primary cooking fuel report improved health, a lower probability of physical discomfort, and higher fitness expenditures than non-users. Clean energy use does not significantly affect self-reported health, the probability of having bronchitis and asthma, or medical expenditures. The health effects of clean energy use on men differ from those on women; they also vary across different economic strata. We also find that farmers’ decisions to use clean energy are positively associated with their educational level, household income, whether they rent farmland, and their happiness levels but are negatively related to their age, family size, whether they own real estate, and the ratio of elders in their household.

Comparative study on gradient-free optimization methods for inverse source-term estimation of radioactive dispersion from nuclear accidents

In this study, we rigorously assess the performance of three gradient-free optimization algorithms—Ensemble Kalman Inversion (EKI), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA)—for estimating source terms in diverse radionuclide release scenarios. Our analysis encompasses both single and multiple sources with varying radionuclide compositions, delving into the influence of decay constants and radioactivity on source estimation accuracy. Although estimating a single radionuclide from a single source exhibits outstanding results, estimating multiple radionuclides from a single source proves more arduous due to the limited information available for discerning gamma dose rates. Contrary to expectations, increasing the number of observation stations does not consistently improve the likelihood of finding accurate solutions in ill-posed inverse problems. Impressively, under our simulation settings, EKI demonstrates competitive performance in terms of convergence, accuracy, and runtime compared to PSO and GA, with GPU parallelization further bolstering computational efficiency. We explore strategies for enhancing source term estimation, including incorporating prior information, applying uncertainty removal techniques, and optimizing observation placement. Additionally, this study underscores the intricate role of relative error in determining multi-radionuclide estimation accuracy from gamma dose measurements. By employing the Gaussian plume model under steady-state conditions, our research lays the groundwork for future applications of Lagrangian dispersion models with real-time data integration. The insights gleaned from our study promise to advance environmental radioactivity monitoring and catalyze the development of cutting-edge, real-time source estimation technologies in full-scale systems.

Metrics for evaluating the quality in linear atmospheric inverse problems: a case study of a trace gas inversion

Abstract. Several metrics have been proposed and utilized to diagnose the performance of linear Bayesian and geostatistical atmospheric inverse problems. These metrics primarily assess the reductions in the prior uncertainties, compare modeled observations to true observations, and check distributional assumptions. Although important, these metrics should be augmented with a sensitivity analysis to obtain a comprehensive understanding of the atmospheric inversion performance and improve the quality and confidence in the inverse estimates. In this study, we derive closed-form expressions of local sensitivities for various input parameters, including measurements, covariance parameters, covariates, and a forward operator. To further enhance our understanding, we complement the local sensitivity analysis with a framework for a global sensitivity analysis that can apportion the uncertainty in input parameters to the uncertainty associated with inverse estimates. Additionally, we propose a mathematical framework to construct nonstationary correlation matrices from a precomputed forward operator, which is closely tied to the overall quality of inverse estimates. We demonstrate the application of our methodology in the context of an atmospheric inverse problem for estimating methane fluxes in Los Angeles, California.

Inversion and forward estimation with process-based models: An investigation into cost functions, uncertainty-based weights and model-data fusion

Process-based models for inversion and forward estimation have supported our understanding of earth, environment, and hazards processes. These methods are often applied on spatial data without accounting for their spatial nature and uncertainty. Using an example of reconstructing past volcanic eruption characteristics and associated tephra fallout from different sets of field observation, we demonstrate the importance of making the best use of data-related uncertainty and spatial information in inversion and forward estimation. We present strategies for: (1) the selection of appropriate cost functions accounting for their behaviour and implied distribution of residuals, (2) the treatment of differential uncertainty when combining multiple data, and (3) the leveraging of both model and data when estimating the spatial distribution of output. Results show that a data-informed choice of cost function and accounting for uncertainty and spatial characteristics of data leads to consistent improvements in model predictive performance for both inversion and forward models.

Causal Effects of Plasma Haptoglobin Levels on Alzheimer's Disease: A Two-Sample Mendelian Randomization Study.

A connection between plasma levels of haptoglobin (Hp) and Alzheimer's disease (AD) has been shown in several observational studies. It is debatable, nonetheless, how the two are related causally. To establish the causal relationship between Hp and AD using a two-sample Mendelian randomization (MR) study. From the extensive genome-wide association studies and FinnGen dataset, summaries and statistics pertaining to AD were gathered. We investigated the possibility of a causal link between Hp and AD using a two-sample MR study. Inverse variance weighting was used as the primary analytical technique, and it was supported by the joint application of complementary analyses and fixed effects meta-analysis to combine results from various sources. Genetically determined Hp was causally associated with AD [odds ratio (OR), 1.05; 95% confidence interval (CI), 1.02 to 1.09; p = 8.96×10-4]; Inverse variance-weighted estimates coming from different data sources were combined in a meta-analysis with consistent findings (OR, 1.03; 95% CI, 1.01 to 1.05; p = 2.00×10-3). The outcomes of the inverse MR analysis showed that AD had no appreciable causal impact on Hp. The present MR analysis shows that higher plasma Hp leads to an increased risk of AD. Strategies for plasma Hp testing may open up new doors for the early diagnosis and prevention of AD.

Simultaneous estimation of thermal and optical properties of molten salt based on improved colliding bodies optimization

Inverse estimation of thermal and optical properties of molten salt is studied in this study. An improved colliding bodies optimization (ICBO) algorithm is developed to solve inverse problems, in which three improvements is proposed: (1) chaos theory is introduced to optimize initial positions of colliding bodies; (2) a self-memory strategy is proposed to improve local search ability; and (3) a random search strategy is proposed to improve global search ability. And the test functions prove that the ICBO algorithm is more efficient and robust than the original colliding bodies optimization algorithm. A two-step retrieval strategy is proposed to overcome serious crosstalk problem among different parameters, in which optical parameters are inversed based on the transmittance-reflectance model firstly, and then thermal parameters are inversed based on the transient coupled conduction-radiation model and known optical parameters obtained in the first step. All research outcomes indicate that the constant and temperature-dependent thermal and optical parameters of molten salt can be estimated accurately by the ICBO algorithm and two-step retrieval strategy. Even if considering 10% of measurement error, the maximum relative error is only 6.50% (estimation of thermal conductivity). Therefore, this work is beneficial for improving utilization of molten salt for harvesting solar energy.

A Functional Data Classification Framework Based on Functional Sufficient Dimension Reduction and Model Averaging

With the ever-evolving advancements in data collection and storage technologies, high-frequency data recorded in patterns that change over time have become increasingly common. In many application scenarios for this type of data, functional data classification has emerged as a prominent issue in the field of statistics. In light of this, this paper proposes a functional data classification model based on the functional sufficient dimensionality reduction method and the idea of model averaging. The proposed method utilizes techniques such as Functional Slice Inverse Regression (FSIR) and Functional Average Variance Estimation (FSAVE) to project an infinite-dimensional random function onto a function space spanned by a finite wiki function, ensuring that the original data's effective information for categorical variables is not lost. Additionally, the Bagging algorithm effectively addresses the overfitting and underfitting problems that arise in single predictive models, while employing model averaging instead of model selection to adaptively select the sufficient dimensionality reduction sub-directions. Notably, in the prediction stage, the number and types of base models are flexible. Empirical analysis demonstrates that the proposed method outperforms some comparative methods in terms of prediction accuracy and robustness.

Inverse ESPVR Estimation with Singularity Avoidance via Constrained EDPVR Parameter Optimization.

Left ventricular end-systolic elastance Ees, as an index of cardiac contractility, can play a key role in continuous patient monitoring during cardiac treatment scenarios such as drug therapies. The clinical feasibility of Ees estimation remains challenging because most techniques have been built on left ventricular pressure and volume, which are difficult to measure or estimate in the regular ICU/CCU setting. The purpose of this paper is to propose and validate a novel approach to estimate Ees, which is independent of left ventricular pressure and volume. Our methods first derive an analytical representation of Ees as the inverse function of the gradient of the Frank-Starling Curve based on cardiac mechanics. Second, elucidating the mechanism of singularities in the inverse function, we derive multiple conditions in both end-systolic pressure-volume relationship (ESPVR) and end-diastolic pressure-volume relationship (EDPVR) parameters to avoid these singularities analytically. Third, we formulate a constrained nonlinear least squares problem to optimize both ESPVR and EDPVR parameters simultaneously to avoid singularities. The effectiveness of the proposed method in avoiding singularities was evaluated in an animal experiment. Compared to the conventional Ees estimation by linear regression, our proposed method reproduced in-vivo hemodynamics more accurately when simulating the estimated Ees variation during drug administration. Our method can be applied using the available data in the regular ICU/CCU setting. The improved clinical feasibility can support not only physicians' decision-making, including adjusting drug dosages in current clinical treatment, but also a closed-loop hemodynamic control system requiring accurate continuous Ees estimation.

A Hybrid Strategy Enhanced Sub-Sampled Recursive Second Order Algorithm for Chemical Process Intelligent Identification

The accurate dynamic model of the chemical process is an important condition for the successful implementation of advanced control in the plant. In this paper, an efficient second order algorithm for long short-term memory (LSTM) network training is proposed for chemical process intelligent identification. A novel Hessian inverse recursion method is adopted to achieve fast convergence and avoid the high-cost operation of the classic second order optimization method. Besides, more information is back propagated since the proposed method retains the real curvature information of the neural network. Considering the large amount of chemical process data, a sub-sampled recursive second order-stochastic gradient descent (SRSO-SGD) algorithm which uses sub-sampling method and hybrid strategy is proposed. The identification experiment on a delayed coker fractionator shows that the proposed sub-sampled neural network second order training algorithm has better performance than other learning algorithms in terms of model identification accuracy and convergence speed. By adopting a hybrid strategy that performing Hessian inverse estimation every 3 training epochs, the expensive Hessian inverse calculation cost in the identification process is further reduced while low training and testing errors are maintained.

Joint inverse estimation of groundwater pollution source characteristics and model parameters based on an intelligent particle filter

Accurate groundwater pollution source estimation (GPSE) is the prerequisite for efficiently designing groundwater pollution remediation strategies and pollution risk assessment. However, deterministic point estimation of pollution source characteristics may not completely solve the problem for practical situations. Therefore, an interval estimation, capable of providing more comprehensive and abundant information for decision-makers, should also be considered. In GPSE, the particle filter (PF) method is a good means for the interval estimation. However, some inherent problems in the general PF method may compromise the estimation accuracy. Thus, this study made two improvements to the general PF method as a solution. First, to solve the problem of its weak local search ability when tackling complex problems, this study embedded the optimization method with strong local search ability into the importance sampling process, to strengthen the local search ability, better optimize the particle position, and guide the overall distribution of particles to move towards the true distribution. Secondly, in the resampling process, all particles need to be resampled in each iteration, which may lead to plunging into the local optima. Thus, this study introduced an intelligent resampling process that intelligently adjusted the number of resampling particles in each iteration to avoid plunging into the local optima. Through two improvements on the PF method, an intelligent PF method was innovatively constructed for GPSE, improving the estimation accuracy. Additionally, to alleviate the computational cost caused by the frequent simulation model invocation, we adopted a deep learning method (deep belief neural network method) to build the surrogate model of the simulation model, ensuring high approximation accuracy to the simulation model. Also, we applied the theories and methods mentioned above to two respective scenarios (inorganic and organic groundwater pollution). We simultaneously estimated the pollution source characteristics and simulation model parameters to verify the effectiveness. Finally, we obtained the point estimation, posterior probability distribution, and interval estimation results, capable of providing a more reliable and comprehensive reference for decision-makers.

Remotely sensed and surface measurement- derived mass-conserving inversion of daily NOx emissions and inferred combustion technologies in energy-rich northern China

Abstract. This work presents a new model-free inversion estimation framework (MFIEF) using daily TROPOspheric Monitoring Instrument (TROPOMI) NO2 columns and observed fluxes from the continuous emission monitoring system (CEMS) to quantify 3 years of daily scale emissions of NOx at 0.05∘×0.05∘ over Shanxi Province, a major world-wide energy-producing and energy-consuming region. The NOx emissions, day-to-day variability, and uncertainty on a climatological basis are computed to be 1.86, 1.03, and 1.05 Tg yr−1 respectively. The highest emissions are concentrated in the lower Fen River valley, which accounts for 25 % of the area, 53 % of the NOx emissions, and 72 % of CEMS sources. Two major forcing factors (10th to 90th percentile) are horizontal transport distance per day (63–508 km) and lifetime of NOx (7.1–18.1 h). Both of these values are consistent with NOx emissions to both the surface layer and the free troposphere. The third forcing factor, the ratio of NOx/NO2, on a pixel-to-pixel basis, is demonstrated to correlate with the combustion temperature and energy efficiency of large energy consuming sources. Specifically, thermal power plants, cement, and iron and steel companies have a relatively high NOx/NO2 ratio, while coking, industrial boilers, and aluminum oxide factories show a relatively lower ratio. Variance maximization is applied to daily TROPOMI NO2 columns, which facilitates identification of three orthogonal and statistically significant modes of variability, and successfully attributes them both spatially and temporally to (a) this work's computed emissions, (b) remotely sensed TROPOMI ultraviolet aerosol index (UVAI), and (c) computed transport based on TROPOMI NO2.

Inverse estimation of a vertical current velocity profile using motions of an FPSO and artificial neural network

This study utilizes artificial neural network (ANN) models to inversely estimate the near-real-time 3D ocean vertical current profile (magnitudes and directions) using floater-mounted motion sensors with (or without) two bi-axial inclinometers on the top portion of a riser. A coupled time-domain dynamics model that takes into account the interaction between the FPSO (Floating Production Storage and Offloading platform), mooring lines, risers, and cables is established to provide inputs and outputs for ANN models. The dynamics simulations are performed using the 2-year-long measured environmental (wind, wave, current) conditions. Then, three ANN models with different combinations of input variables are evaluated, and the process of hyperparameter selection is conducted to increase the prediction accuracy compared to the actually measured current profiles. The root-mean-square errors (RMSEs) and R-squared of current speed, the contour plots of measured and estimated current speed and direction, representative motions of the FPSO, and inclination and azimuthal angles of a riser are systematically presented. The results demonstrate that the proposed ANN model adequately estimates the 3D current profile from FPSO motions while the accuracy especially at deeper depth is improved by considering bi-axial inclinometers on the riser as additional inputs.

Biases in inverse Ising estimates of near-critical behavior.

Inverse Ising inference allows pairwise interactions of complex binary systems to be reconstructed from empirical correlations. Typical estimators used for this inference, such as pseudo-likelihood maximization (PLM), are biased. Using the Sherrington-Kirkpatrick model as a benchmark, we show that these biases are large in critical regimes close to phase boundaries, and they may alter the qualitative interpretation of the inferred model. In particular, we show that the small-sample bias causes models inferred through PLM to appear closer to criticality than one would expect from the data. Data-driven methods to correct this bias are explored and applied to a functional magnetic resonance imaging data set from neuroscience. Our results indicate that additional care should be taken when attributing criticality to real-world data sets.

A Deep Reinforcement Learning Approach Combined With Model-Based Paradigms for Multiagent Formation Control With Collision Avoidance

Generating collision-free formation control strategy for multiagent systems faces huge challenges in collaborative navigation tasks, especially in a highly dynamic and uncertain environment. Two typical methodologies for solving this problem are the conventional model-based paradigm and the data-driven paradigm, particularly the widely used deep reinforcement learning (DRL) method. However, both the model-based and data-driven paradigms encounter inherent drawbacks. In this paper, we present two novel general schemes that combine these two paradigms together in an online mode. Specifically, the two paradigms are combined in a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">parallel</i> and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">serial</i> structure in these two schemes, respectively. In the parallel scheme, the outputs of the model-based and DRL-based controllers are lumped together. In the serial scheme, the output of the model-based controller is fed as an input of the DRL-based controller. The interpretation of the two combined schemes is suggested from a control-oriented perspective, where the parallel DRL controller is viewed as a complementary uncertainty compensator and the serial DRL controller is taken as an inverse dynamics estimator. Finally, comprehensive simulations are conducted to demonstrate the superiority of the proposed schemes, and the effectiveness is further verified by deploying our schemes to a physical experiment platform based on a set of three-wheeled omnidirectional robots.

High-Speed Maneuvering Target Inverse Synthetic Aperture Radar Imaging and Motion Parameter Estimation Based on Fast Spare Bayesian Learning and Minimum Entropy

High-speed maneuvering target inverse synthetic aperture radar (ISAR) imaging is always a hot topic in signal processing. High-speed maneuvering targets often have high-order maneuvering characteristics, such as translational and rotational characteristics, which destroy the signal structure of stationary targets and make basic imaging processing methods such as range-Doppler (RD) algorithm no longer suitable. In this paper, a high-resolution imaging method for high-speed maneuvering targets is proposed, which uses the fast sparse Bayesian learning (SBL) algorithm and the minimum entropy algorithm for ISAR high-resolution imaging and motion parameter estimation, respectively. Because SBL makes full use of the characteristics of the target and the environment, it can obtain an ISAR high-resolution image of the maneuvering target. However, the high computational complexity caused by matrix inversion and some matrix operations in SBL iteration limit the practical application of SBL in ISAR imaging. In view of the special structure of the matrix required to be inverted, we propose a fast SBL algorithm, which uses a new decomposition method to obtain the decomposition formula of the inverse matrix. Based on the decomposition factors, the multiplication operation involving the inverse matrix can be quickly calculated using fast Fourier transform (FFT), which greatly improves the computational efficiency. Image entropy represents the sharpness and focusing degree of an image, and so the minimum entropy algorithm can estimate the motion parameters of maneuvering targets more accurately. We combine the minimum entropy algorithm with the fast SBL algorithm to realize phase error correction and high-resolution imaging, which has better noise sensitivity and can obtain the best focusing degree image. Finally, simulation results prove the effectiveness of this algorithm.