Journal articles: 'Fixed-Time and robust estimation'

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OLIINYK, VIACHESLAV, and VOLODYMYR LUKIN. "USE OF SIMILARITY METRICS IN ROBUST TIME DELAY ESTIMATION." Herald of Khmelnytskyi National University. Technical sciences 319, no. 2 (April 27, 2023): 224–30. http://dx.doi.org/10.31891/2307-5732-2023-319-1-224-230.

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This paper addresses the task of time delay and direction of arrival estimation for a source of the wideband signal using two sensors with fixed displacement. The peculiarity of the task statement is that a limited time of signal observation is supposed and additive noise is assumed non-Gaussian with a heavy-tail distribution. This leads to a high probability of abnormal estimates for the conventional signal processing method based on cross-correlation. To decrease this probability, it is proposed to reformulate the task of cross-correlation processing to the task of similarity estimation between two data arrays. This allows using different similarity metrics, particularly those that have less sensitivity to outliers in data (impulse noise), and, thus, provide better robustness for non-Gaussian environments typical for several applications of time delay estimation. More than ten different similarity metrics are considered for the model of the symmetric α-stable distribution describing noise properties. It is shown that some metrics including cosine distance, Hellinger, and some others are able to provide sufficiently better accuracy of time delay estimation both in the sense of less RMSE of normal estimates and probability of abnormal estimates for typical values of α and a wide range of γ values for symmetric α-stable distribution.

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Thombs, Ryan P. "A Guide to Analyzing Large N, Large T Panel Data." Socius: Sociological Research for a Dynamic World 8 (January 2022): 237802312211176. http://dx.doi.org/10.1177/23780231221117645.

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Fixed effects estimation of a static model with robust or panel corrected standard errors is commonly used to model large N, large T panel data. However, this approach is biased and inconsistent in the presence of dynamic misspecification, slope heterogeneity, and cross-sectional dependence. Common correlated effects estimation of a dynamic model has been advanced to address these issues but is rarely used in sociology. Here, I provide an overview of the large N, large T panel data literature, and I conduct an array of Monte Carlo experiments to compare the fixed effects estimator to the common correlated effects estimator regarding the aforementioned issues. I show that fixed effects estimation with robust or panel corrected standard errors do not address these problems, which is most evident with high levels of slope heterogeneity and lag misspecification, and its performance worsens as the time dimension expands. In contrast, the common correlated effects estimator produces superior estimates as T increases and is robust to slope heterogeneity and cross-sectional dependence. Following the experiments, I present an example by examining the drivers of fossil fuel consumption at the U.S. state level from 1960 to 2018, and I conclude by presenting a decision-making framework for researchers to use to make informed decisions when modeling large N, large T panel data.

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Wu, Tao, Zhengjiang Liu, and Guoyou Shi. "Practical Fixed-Time Robust Containment Control of Multi-ASVs with Collision Avoidance." Journal of Marine Science and Engineering 12, no. 12 (December 23, 2024): 2363. https://doi.org/10.3390/jmse12122363.

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A practical fixed-time robust containment control method for multiple autonomous surface vehicles (multi-ASVs) is proposed in this study. This method addresses the containment control problem of multi-ASVs, considering both collision risks and external disturbances. This control scheme improves the cooperative performance of the formation and guarantees safe collision avoidance behavior. First, to enable the online estimation of unknown time-varying disturbances from the external environment, a fixed-time disturbance observer (FNDO) is designed based on fixed-time control theory. Second, the distributed kinematic controller is modified to include the partial derivatives of the artificial potential energy function (APEF), thereby preventing collisions among multi-ASVs. Third, by applying fixed-time theory, graph theory, and fixed-time dynamic surface control techniques, a practical fixed-time robust containment controller for multi-ASVs is proposed. Additionally, the entire closed-loop control system is guaranteed to be practical and fixed-time stable through stability analysis. Finally, the proposed control strategy has been validated by simulation results.

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Youssef, Ahmed Hassen, Mohamed Reda Abonazel, and Elsayed G. Ahmed. "Robust M Estimation for Poisson Panel Data Model with Fixed Effects: Method, Algorithm, Simulation, and Application." Statistics, Optimization & Information Computing 12, no. 5 (June 3, 2024): 1292–305. http://dx.doi.org/10.19139/soic-2310-5070-1996.

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The fixed effects Poisson (FEP) model is one of the most important for the count data when the data containperiods and cross-sectional units. The maximum likelihood (ML) estimation method for the FEP model provides good results in the absence of outliers, but it is affected by outliers. So, we introduce in this paper robust estimators for the FEP model. These estimators yield stable and good results in case of the presence of outliers. The Monte Carlo simulation study and empirical application were conducted to assess the performance of the non-robust fixed Poisson maximum likelihood (FPML) estimator and the robust estimators: fixed Poisson Huber (FPHR), fixed Poisson Hampel (FPHM) and fixed Poisson Tukey (FPTK). The results of simulation and application show that robust estimators are better than FPML estimator when the count panel data contains outliers. In addition, FPTK is more efficient than other robust estimators.

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Magnussen, Steen. "Robust fixed-count density estimation with virtual plots." Canadian Journal of Forest Research 44, no. 4 (April 2014): 377–82. http://dx.doi.org/10.1139/cjfr-2013-0288.

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Fixed-count sampling (plotless) remains attractive for forest inventories in difficult terrains and for their control of the number (k) of trees to measure. Although recent fixed-count estimators of density (PDE) are less biased than older ones, the risk of a nontrivial bias remains a deterrent. A recently published PDE based on a generic algorithm for predicting distances to the k + m nearest tree (m = 1, 2, ...) has attractive properties in terms of average bias and average root mean squared errors across a wide spectrum of spatial point patterns. However, the risk of a sizeable bias remains an issue. Sensitivity to spatial patterns is seen as its main weakness. It is hypothesized that a new PDE with robust properties will mitigate the bias issue and encourage wider use. To this end, a new PDE estimator is proposed. It builds on a mixture of observed and predicted distances to a set of k + m nearest trees to generate counts of actual and virtual trees inside a circle with a data-driven fixed radius. The proposed new robust fixed-count density estimator achieved an average absolute bias of 1.2% when tested across a wide range of point patterns (54 actual and four simulated). The maximum absolute bias was 4.4%, a significant reduction when compared with otherwise attractive alternative PDEs. Root mean squared errors and coverage of 95% confidence intervals were also encouraging. The deterrent bias issue in PDEs has been sharply reduced with the proposed estimator.

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Chai, Dashuai, Yipeng Ning, Shengli Wang, Wengang Sang, Jianping Xing, and Jingxue Bi. "A Robust Algorithm for Multi-GNSS Precise Positioning and Performance Analysis in Urban Environments." Remote Sensing 14, no. 20 (October 15, 2022): 5155. http://dx.doi.org/10.3390/rs14205155.

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In this paper, we propose a partial ambiguity method of global navigation satellite system (GNSS) reliable positioning based on a robust estimation model to address the problems of the low reliability and availability of GNSS positioning in urban complex environments. First, the high-precision observations selected on the basis of the signal-to-noise ratio (SNR) were used to solve ambiguities. Then, the fixed ambiguities were used as constraints to solve the ambiguities of low-quality observations. The robust estimation method was used to reduce the impact of outliers for the ambiguity solutions. The robust estimation was also used to solve the position parameters to reduce the influence of the residual errors and uncorrected ambiguities for GNSS high-accuracy positioning. Static and dynamic data were used to evaluate the proposed algorithm. These experiments show that the proposed algorithm with the robust estimation can reduce the fixed time of ambiguity initialization, compared with the conventional algorithm without the robust estimation. The positioning accuracy and solution rate are similar regardless of whether the robust estimation is used in the GNSS unblocked environment. In blocked environments, the solution rate improves to more than 99%, and the three-dimensional (3D) position accuracy improves by more than 70% when the robust estimation is used. When the observation number of simulated small gross error accounts for 40.91% of total observations, the centimeter-level positioning accuracy can still be obtained via several robust estimation models. In the urban blocked environment, the IGG (Institute of Geodesy and Geophysics) III scheme has a better performance than other robust schemes discussed in this paper with regard to the positioning performance and computational efficiency.

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Xi, Axing, and Yuanli Cai. "A Nonlinear Finite-Time Robust Differential Game Guidance Law." Sensors 22, no. 17 (September 2, 2022): 6650. http://dx.doi.org/10.3390/s22176650.

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In this paper, a robust differential game guidance law is proposed for the nonlinear zero-sum system with unknown dynamics and external disturbances. First, the continuous-time nonlinear zero-sum differential game problem is transformed into solving the nonlinear Hamilton–Jacobi–Isaacs equation, a time-varying cost function is developed to reflect the fixed terminal time, and the robust guidance law is developed to compensate for the external disturbance. Then, a novel neural network identifier is designed to approximate the unknown nonlinear dynamics with online weight tuning. Subsequently, an online critic neural network approximator is presented to estimate the cost function, and time-varying activation functions are considered to deal with the fixed final time problem. An adaptive weight tuning law is given, where two additional terms are added to ensure the stability of the closed-loop nonlinear system and so as to meet the terminal cost at a fixed final time. Furthermore, the uniform ultimate boundedness of the closed-loop system and the critic neural network weights estimation error are proven based upon the Lyapunov approach. Finally, some simulation results are presented to demonstrate the effectiveness of the proposed robust differential game guidance law for nonlinear interception.

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Nakamori, Seiichi. "Robust Recursive Least-Squares Fixed-Point Smoother and Filter using Covariance Information in Linear Continuous-Time Stochastic Systems with Uncertainties." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 20 (May 13, 2024): 56–66. http://dx.doi.org/10.37394/232014.2024.20.2.

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This study develops robust recursive least-squares (RLS) fixed-point smoothing and filtering algorithms for signals in linear continuous-time stochastic systems with uncertainties. The algorithms use covariance information, such as the cross-covariance function of the signal with the observed value and the autocovariance function of the degraded signal. A finite Fourier cosine series expansion approximates these functions. Additive white Gaussian noise is present in the observation of the degraded signal. A numerical simulation compares the estimation accuracy of the proposed robust RLS filter with the robust RLS Wiener filter, showing similar mean square values (MSVs) of the filtering errors. The MSVs of the proposed robust RLS fixed-point smoother are also compared to those of the proposed robust RLS filter.

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Mentz, Raúl P., and Carlos I. Martínez. "Robust estimation in time series." Test 11, no. 2 (December 2002): 385–404. http://dx.doi.org/10.1007/bf02595713.

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Guerrier, Stephane, Roberto Molinari, and Maria-Pia Victoria-Feser. "Estimation of Time Series Models via Robust Wavelet Variance." Austrian Journal of Statistics 43, no. 4 (June 13, 2014): 267–77. http://dx.doi.org/10.17713/ajs.v43i4.45.

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A robust approach to the estimation of time series models is proposed. Taking froma new estimation method called the Generalized Method of Wavelet Moments (GMWM)which is an indirect method based on the Wavelet Variance (WV), we replace the classicalestimator of the WV with a recently proposed robust M-estimator to obtain a robustversion of the GMWM. The simulation results show that the proposed approach can beconsidered as a valid robust approach to the estimation of time series and state-spacemodels.

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Zhao, Hai Yan, and Wen Bai Li. "Energy-to-Peak Mode-Dependent Filter Design for Discrete-Time Markovian Jump Linear Systems with Intermittent Measurements." Advanced Materials Research 580 (October 2012): 244–47. http://dx.doi.org/10.4028/www.scientific.net/amr.580.244.

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In this paper, we investigate the robust energy-to-peak filtering for discrete-time Markovian jump linear systems with intermittent measurements. The intermittent measurements are modeled as a Bernoulli processwith a fixed probability. By defining the filtering error, an augmented estimation error system is obtained. Due to the existence of the stochastic variables, the exponentially mean-square stability and the robust energy-to-peak performance are both studied for the estimation error system. The widely-used Lyapunov theory is employed to investigate the stability and the energy-to-peak performance. Sufficient conditions expressed in the form of matrix inequalities are obtained. Since the estimator parameters and the Lyapunov weighting matrix to be determined are coupled with each other, a partition technique is utilized to decouple the estimator parameters and the Lyapunov weighting matrix. After defining several new parameters, we develop the design approach for the mode-dependent energy-to-peak estimator. The estimator parameters can be derived by solving a set of linear matrix inequalities. Finally, a numerical example is used to show the effectiveness of the proposed design approach.

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Guo, Ying, and Cai Yun Meng. "Adaptive Time Delay Estimation Based on Robust Cyclic Correlation Estimator." Applied Mechanics and Materials 229-231 (November 2012): 1919–22. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.1919.

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Based on the proposed efficient cyclic correlation estimator by exploiting normalized infinite norm, this paper develops a robust adaptive time delay estimation method which provides additional robustness against heavy-tailed noise and when the noise statistics are not fully known. Simulations demonstrate the reliable and robust performance of the proposed cyclic correlation estimator and its corresponding time delay estimation algorithm.

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You-Seok Lee, Hyoung-Nam Kim, and Kyung Sik Son. "Noise-robust channel estimation for DVB-T fixed receptions." IEEE Transactions on Consumer Electronics 53, no. 1 (February 2007): 27–32. http://dx.doi.org/10.1109/tce.2007.339497.

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Aquaro, Michele, and Pavel Čížek. "Robust estimation of dynamic fixed-effects panel data models." Statistical Papers 55, no. 1 (July 5, 2013): 169–86. http://dx.doi.org/10.1007/s00362-013-0545-7.

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Nakamori, Seiichi. "Robust Estimators for Missing Observations in Linear Discrete-Time Stochastic Systems with Uncertainties." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 19 (December 29, 2023): 168–83. http://dx.doi.org/10.37394/232014.2023.19.18.

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As a first approach to estimating the signal and the state, Theorem 1 proposes recursive least-squares (RLS) Wiener fixed-point smoothing and filtering algorithms that are robust to missing measurements in linear discrete-time stochastic systems with uncertainties. The degraded quantity is given by multiplying the Bernoulli random variable by the degraded signal caused by the uncertainties in the system and observation matrices. The degraded quantity is observed with additional white observation noise. The probability that the degraded signal is present in the observation equation is assumed to be known. The design feature of the proposed robust estimators is the fitting of the degraded signal to a finite-order autoregressive (AR) model. Theorem 1 is transformed into Corollary 1, which expresses the covariance information in a semi-degenerate kernel form. The autocovariance function of the degraded state and the cross-covariance function between the nominal state and the degraded state is expressed in semi-degenerate kernel forms. Theorem 2 shows the robust RLS Wiener fixed-point and filtering algorithms for estimating the signal and state from degraded observations in the second method. The robust estimation algorithm of Theorem 2 has the advantage that, unlike Theorem 1 and the usual studies, it does not use information on the existence probability of the degraded signal. This is a unique feature of Theorem 2.

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Mondiana, Yani Quarta, Henny Pramoedyo, Atiek Iriany, and Marjono. "Applied fixed effect of Geographically Weighted Panel Regression (GWPR) with M- Estimator approach to estimate sugarcane yield data in East Java." Journal of Applied and Natural Science 16, no. 2 (June 19, 2024): 646–52. http://dx.doi.org/10.31018/jans.v16i2.5443.

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Geographically Weighted Panel Regression (GWPR), a combination of panel regression and geographically weighted regression (GWR), is used to analyze panel data and capture diverse relationship between locations. GWPR was developed on data with panel-fixed effects and applied to modeling data with spatial heterogeneity and time series. One method for estimating parameters in the GWPR is weighted least squares (WLS), which are sensitive to outliers. The present study aimed to use the M- method to estimate GWPR model parameters in data containing outliers using fixed-effect GWPR modeling for the sugar cane yield in East Java of Indonesia from 2019 to 2021. Sugarcane yield data in East Java contained outliers in several areas, including Malang, Blitar, and Ngawi Districts. Because the data contains outliers, a robust method with the M estimator was applied. The results showed that plantation areas significantly affected production in all districts.The R2 of the model was 0.87, showing that GWPR model with M estimation was appropriate in predicting sugarcane yield. Based on the Akaike Information Criterion (AIC) value, the GWPR model with M estimation had better performance than GWPR model alone.

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Namah Jaseem, Haneen, and Lekaa Ali Mohammad. "Detecting Outliers and Using Robust Methods in Linear Panel Data Model." Al-Nahrain Journal of Science 27, no. 4 (October 1, 2024): 40–46. https://doi.org/10.22401/anjs.27.4.07.

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The increasing use of panel data across various fields necessitates robust estimation methods that can resist the influence of outliers, which often lead to biased and ineffective estimates when using traditional methods like least squares. This research investigates two robust estimation techniques within fixed and random effects models for panel data, comparing their performance using the mean square error. Through a simulation experiment with varying sample sizes and contamination levels, the results for the fixed effects model indicate that the Weighted Likelihood Estimator consistently outperformed other methods across all sample sizes at a 10% contamination rate, while the S method excelled at a 20% contamination rate. For the random effects model, the former was most effective with a sample size of 200, while the latter proved superior at a sample size of 800, regardless of contamination levels.

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Rsetam, Kamal, Zhenwei Cao, Lulu Wang, Mohammad Al-Rawi, and Zhihong Man. "Practically Robust Fixed-Time Convergent Sliding Mode Control for Underactuated Aerial Flexible JointRobots Manipulators." Drones 6, no. 12 (December 19, 2022): 428. http://dx.doi.org/10.3390/drones6120428.

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The control of an aerial flexible joint robot (FJR) manipulator system with underactuation is a difficult task due to unavoidable factors, including, coupling, underactuation, nonlinearities, unmodeled uncertainties, and unpredictable external disturbances. To mitigate those issues, a new robust fixed-time sliding mode control (FxTSMC) is proposed by using a fixed-time sliding mode observer (FxTSMO) for the trajectory tracking problem of the FJR attached to the drones system. First, the underactuated FJR is comprehensively modeled and converted to a canonical model by employing two state transformations for ease of the control design. Then, based on the availability of the measured states, a cascaded FxTSMO (CFxTSMO) is constructed to estimate the unmeasurable variables and lumped disturbances simultaneously in fixed-time, and to effectively reduce the estimation noise. Finally, the FxTSMC scheme for a high-order underactuated FJR system is designed to guarantee that the system tracking error approaches to zero within a fixed-time that is independent of the initial conditions. The fixed-time stability of the closed-loop system of the FJR dynamics is mathematically proven by the Lyapunov theorem. Simulation investigations and hardware tests are performed to demonstrate the efficiency of the proposed controller scheme. Furthermore, the control technique developed in this research could be implemented to the various underactuated mechanical systems (UMSs), like drones, in a promising way.

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Pietak, Lukasz. "Structural Funds and Convergence in Poland." Revista Hacienda Pública Española 236, no. 1 (March 2021): 3–37. http://dx.doi.org/10.7866/hpe-rpe.21.1.1.

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This study aimed to investigate whether cohesion policy funds affected economic growth and resulted in a convergence process across NUTS 2 regions in Poland. The study examined a balanced panel of data from 16 Polish regions between 2004 and 2016 and applied different estimations procedures, in-cluding the fixed effects estimator –as the main estimator– and the GMM estimator. The empirical analysis revealed that cohesion policy funds had a positive impact on regional economic growth. How-ever, the influence of structural funds on convergence was shown to be weak in Poland. The results were robust to various estimation methods.

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Kovačević, B. D., M. D. Veinović, and M. M. Milosavljević. "Robust Time-Varying AR Parameter Estimation." IFAC Proceedings Volumes 29, no. 1 (June 1996): 4640–45. http://dx.doi.org/10.1016/s1474-6670(17)58414-1.

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Fiteni, Inmaculada. "ROBUST ESTIMATION OF STRUCTURAL BREAK POINTS." Econometric Theory 18, no. 2 (April 2002): 349–86. http://dx.doi.org/10.1017/s0266466602182065.

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This paper proposes robust M-estimators of dynamic linear models with a structural break of unknown location. Rates of convergence and limiting distributions for the estimated shift point and the estimated regression parameters are derived. The analysis is carried out in the framework of possibly dependent observations and also with trending regressors. The asymptotic distribution of the break location estimator is obtained both for fixed magnitude of shift and for shift with magnitude converging to zero as the sample size increases. The latter is essential for the derivation of feasible confidence intervals for the break location. Monte Carlo simulations illustrate the performance of asymptotic inferences in practice.

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Polat, Ali, and Mehmet Yesilyaprak. "Export Credit Insurance and Export Performance: An Empirical Gravity Analysis for Turkey." International Journal of Economics and Finance 9, no. 8 (July 5, 2017): 12. http://dx.doi.org/10.5539/ijef.v9n8p12.

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The paper attempts to find out how far Turkey’s official export credit agency, Turk Eximbank, foster export of Turkey during the years of 2000-2015 by employing an empirical trade gravity equation. We estimate different panel gravity regressions for 212 countries for the period of 16 years and the results reveal that a change in export credit insurance positively affect Turkish export, assuming other independent variables are held constant. After applying several post estimation tests we used fixed effect panel specification as the main estimation. In order to allow comparison we also run clustered, robust OLS. Poisson fixed effect (Poisson) and Poisson Pseudo maximum likelihood estimations (PPML) are also estimated to allow for zero trade values in dependent variable in its level. Our analysis also shows that there are significant individual and time effects in panel data structure. Our estimate of different panel gravity regressions for 212 countries and 16 years revealed that increasing export insurance will positively affect Turkish export.

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Fu, Liya, You-Gan Wang, and Fengjing Cai. "A working likelihood approach for robust regression." Statistical Methods in Medical Research 29, no. 12 (July 14, 2020): 3641–52. http://dx.doi.org/10.1177/0962280220936310.

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Robust approach is often desirable in presence of outliers for more efficient parameter estimation. However, the choice of the regularization parameter value impacts the efficiency of the parameter estimators. To maximize the estimation efficiency, we construct a likelihood function for simultaneously estimating the regression parameters and the tuning parameter. The “working” likelihood function is deemed as a vehicle for efficient regression parameter estimation, because we do not assume the data are generated from this likelihood function. The proposed method can effectively find a value of the regularization parameter based on the extent of contamination in the data. We carry out extensive simulation studies in a variety of cases to investigate the performance of the proposed method. The simulation results show that the efficiency can be enhanced as much as 40% when the data follow a heavy-tailed distribution, and reaches as high as 468% for the heteroscedastic variance cases compared to the traditional Huber’s method with a fixed regularization parameter. For illustration, we also analyzed two datasets: one from a diabetics study and the other from a mortality study.

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Aquaro, M., and P. Čížek. "One-step robust estimation of fixed-effects panel data models." Computational Statistics & Data Analysis 57, no. 1 (January 2013): 536–48. http://dx.doi.org/10.1016/j.csda.2012.07.003.

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Gao, Chao, Guorong Zhao, Jianhua Lu, and Shuang Pan. "Decentralized state estimation for networked spatial-navigation systems with mixed time-delays and quantized complementary measurements: The moving horizon case." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 11 (June 8, 2017): 2160–77. http://dx.doi.org/10.1177/0954410017712277.

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In this paper, the navigational state estimation problem is investigated for a class of networked spatial-navigation systems with quantization effects, mixed time-delays, and network-based observations (i.e. complementary measurements and regional estimations). A decentralized moving horizon estimation approach, featuring complementary reorganization and recursive procedure, is proposed to tackle this problem. First, through the proposed reorganized scheme, a random delayed system with complementary observations is reconstructed into an equivalent delay-free one without dimensional augment. Second, with this equivalent system, a robust moving horizon estimation scheme is presented as a uniform estimator for the navigational states. Third, for the demand of real-time estimate, the recursive form of decentralized moving horizon estimation approach is developed. Furthermore, a collective estimation is obtained through the weighted fusion of two parts, i.e. complementary measurements based estimation, and regional estimations directly from the neighbors. The convergence properties of the proposed estimator are also studied. The obtained stability condition implicitly establishes a relation between the upper bound of the estimation error and two parameters, i.e. quantization density and delay occur probability. Finally, an application example to networked unmanned aerial vehicles is presented and comparative simulations demonstrate the main features of the proposed method.

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Kojabadi, Hossein Madadi, and Liuchen Chang. "Online induction motor rotor time constant estimation using perturbation-based extremum seeking control." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 3 (September 1, 2022): 1459. http://dx.doi.org/10.11591/ijpeds.v13.i3.pp1459-1468.

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<p class="Abstract"><span lang="EN-US">A novel online induction motor (IM) rotor time constant estimator based on a perturbation-based extremum seeking control (ESC) and model reference adaptive system (MRAS) is proposed. Since implementations of fieldoriented control (FOC) requires accurate values of IM parameters, such as rotor time constant, so accurate and robust on-line estimations of such parameters are crucial for any modern industrial IM drives. The proposed MRAS estimator employs ESC method to estimate the IM rotor resistance in various operating conditions. Meanwhile, since extremum seeking control is a model-free scheme, so it is robust to other IM parameter variations. The feasibility and effectiveness of the IM rotor resistance estimation by utilizing ESC scheme has been verified by simulation and experimental results. A 2.2 kW experimental setup has been implemented. </span></p>

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Yang, Yu, Jing Xiong, Xiaoyu She, Chang Liu, ChengWei Yang, and Jie Li. "Passive Initialization Method Based on Motion Characteristics for Monocular SLAM." Complexity 2019 (February 5, 2019): 1–11. http://dx.doi.org/10.1155/2019/8176489.

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Visual SLAM techniques have proven to be effective methods for estimating robust position and attitude in the field of robotics. However, current monocular SLAM algorithms cannot guarantee timeliness of system startup due to the problematic initialization time and the low success rates. This paper introduces a rectilinear platform motion hypothesis and thereby converts the estimation problem into a verification problem to achieve fast monocular SLAM initialization. The proposed method is simulation tested on a fixed-wing UAV. Tests show that the proposed method can produce faster initialization of visual SLAM and that the advantages are more profound on systems with sparse image features.

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Shao, Q. "Robust Estimation For Periodic Autoregressive Time Series." Journal of Time Series Analysis 29, no. 2 (March 2008): 251–63. http://dx.doi.org/10.1111/j.1467-9892.2007.00555.x.

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Cipra, T. "Robust recursive estimation in nonlinear time series." Communications in Statistics - Theory and Methods 27, no. 5 (January 1998): 1071–82. http://dx.doi.org/10.1080/03610929808832146.

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Gabr, M. M. "Robust estimation of bilinear time series models." Communications in Statistics - Theory and Methods 27, no. 1 (January 1998): 41–53. http://dx.doi.org/10.1080/03610929808832649.

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Sinha, Sanjoy K. "Robust estimation in accelerated failure time models." Lifetime Data Analysis 25, no. 1 (February 13, 2018): 52–78. http://dx.doi.org/10.1007/s10985-018-9421-z.

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Correia, Sergio, Paulo Guimarães, and Tom Zylkin. "Fast Poisson estimation with high-dimensional fixed effects." Stata Journal: Promoting communications on statistics and Stata 20, no. 1 (March 2020): 95–115. http://dx.doi.org/10.1177/1536867x20909691.

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In this article, we present ppmlhdfe, a new command for estimation of (pseudo-)Poisson regression models with multiple high-dimensional fixed effects (HDFE). Estimation is implemented using a modified version of the iteratively reweighted least-squares algorithm that allows for fast estimation in the presence of HDFE. Because the code is built around the reghdfe package ( Correia, 2014 , Statistical Software Components S457874, Department of Economics, Boston College), it has similar syntax, supports many of the same functionalities, and benefits from reghdfe‘s fast convergence properties for computing high-dimensional leastsquares problems. Performance is further enhanced by some new techniques we introduce for accelerating HDFE iteratively reweighted least-squares estimation specifically. ppmlhdfe also implements a novel and more robust approach to check for the existence of (pseudo)maximum likelihood estimates.

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Antončič, Papič, and Blažič. "Robust and Fast State Estimation for Poorly-Observable Low Voltage Distribution Networks Based on the Kalman Filter Algorithm." Energies 12, no. 23 (November 22, 2019): 4457. http://dx.doi.org/10.3390/en12234457.

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This paper presents a novel approach for the state estimation of poorly-observable low voltage distribution networks, characterized by intermittent and erroneous measurements. The developed state estimation algorithm is based on the Extended Kalman filter, where we have modified the execution of the filtering process. Namely, we have fixed the Kalman gain and Jacobian matrices to constant matrices; their values change only after a larger disturbance in the network. This allows for a fast and robust estimation of the network state. The performance of the proposed state-estimation algorithm is validated by means of simulations of an actual low-voltage network with actual field measurement data. Two different cases are presented. The results of the developed state estimator are compared to a classical estimator based on the weighted least squares method. The comparison shows that the developed state estimator outperforms the classical one in terms of calculation speed and, in case of spurious measurements errors, also in terms of accuracy.

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Du, Xiaolei, Huabo Liu, and Haisheng Yu. "Event-Triggered Robust Fusion Estimation for Multi-Sensor Time-Delay Systems with Packet Drops." Applied Sciences 13, no. 15 (July 29, 2023): 8778. http://dx.doi.org/10.3390/app13158778.

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This paper investigates the robust fusion estimation problem for multi-sensor systems with communication constraints, parameter uncertainty, d-step state delays, and deterministic control inputs. The multi-sensor system consists of a fusion center and some sensor nodes with computational capabilities, between which there are random packet drops. The state augmentation method is utilized to transform a time-delay system into a non-time-delay one. The robust state estimation algorithm is derived based on the sensitivity penalty for each sensor node to reduce the impact of modelling errors, and modelling errors here are not limited to a unique form, which implies that the fusion estimator applies to a wide range of situations. An event-triggered transmission strategy has been adopted to effectively alleviate the communication burden from the sensor node to the fusion center. Moreover, the fusion estimator handles packet drops arising from unreliable channels, and the corresponding pseudo-cross-covariance matrix is provided. Some conditions are given to ensure that the estimation error of the robust fusion estimator is uniformly bounded. Two sets of numerical simulations are provided to illustrate the effectiveness of the derived fusion estimator.

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Hoechle, Daniel. "Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence." Stata Journal: Promoting communications on statistics and Stata 7, no. 3 (September 2007): 281–312. http://dx.doi.org/10.1177/1536867x0700700301.

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I present a new Stata program, xtscc, that estimates pooled ordinary least-squares/weighted least-squares regression and fixed-effects (within) regression models with Driscoll and Kraay (Review of Economics and Statistics 80: 549–560) standard errors. By running Monte Carlo simulations, I compare the finite-sample properties of the cross-sectional dependence–consistent Driscoll–Kraay estimator with the properties of other, more commonly used covariance matrix estimators that do not account for cross-sectional dependence. The results indicate that Driscoll–Kraay standard errors are well calibrated when cross-sectional dependence is present. However, erroneously ignoring cross-sectional correlation in the estimation of panel models can lead to severely biased statistical results. I illustrate the xtscc program by considering an application from empirical finance. Thereby, I also propose a Hausman-type test for fixed effects that is robust to general forms of cross-sectional and temporal dependence.

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Zhang, Tie, and Aimin Zhang. "Robust Finite-Time Tracking Control for Robotic Manipulators with Time Delay Estimation." Mathematics 8, no. 2 (January 28, 2020): 165. http://dx.doi.org/10.3390/math8020165.

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In this study, a robust H∞ finite-time tracking controller is proposed for robotic manipulators based on time delay estimation. In this controller, there is no need to know the dynamics of robots, so it is quite simple. The high-gain observer is employed to estimate the joint velocities, which makes it much lower in cost. The theorem proof shows that the closed-loop system is finite-time stable and has a L2 gain that is less than or equal to γ, which shows high accuracy and strong robustness to estimation errors and external disturbances. Simulations on a two-link robot illustrate the effectiveness and advantages of the proposed controllers.

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Alinaghi Hosseinabadi, Pooyan, Ali Soltani Sharif Abadi, Saad Mekhilef, and Hemanshu Roy Pota. "Fixed-Time Adaptive Robust Synchronization with a State Observer of Chaotic Support Structures for Offshore Wind Turbines." Journal of Control, Automation and Electrical Systems 32, no. 4 (April 21, 2021): 942–55. http://dx.doi.org/10.1007/s40313-021-00707-y.

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AbstractThe chaotic support structures for offshore wind turbines are often subjected to a severe environment. A robust control scheme needs to be considered to maintain them in a safe operational limit. Robust sliding mode control (SMC) scheme can provide an excellent robust controller against this severe and challenging environment for these chaotic structures. This paper proposes a novel fixed-time adaptive sliding mode control scheme with a state observer to synchronize chaotic support structures for offshore wind turbines in the presence of matched parametric uncertainties. The proposed controller is a new integration of adaptive control concept, SMC method, fixed-time stability concept and a state observer. A fixed-time stability concept is used to provide stability for the system within a presented time regardless of initial conditions. The adaptive concept is utilized to provide an online estimator of the uncertain upper bound. Also, a nonlinear observer is employed to provide an online estimator of an unmeasured state in the controller. Lyapunov stability theorem is used to analyze fixed-time stability of the system based on SMC methodology. The simulation results demonstrate that the proposed controller is able to ensure fixed-time synchronization along with providing precise means to estimate the unmeasured state as well as uncertainty upper bound.

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Anchieta, David C., and John Buck. "Robust power spectral density estimation via a performance-weighted blend of order statistics." Journal of the Acoustical Society of America 154, no. 4_supplement (October 1, 2023): A209. http://dx.doi.org/10.1121/10.0023303.

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Loud transients introduce bias to background spectrum estimates based on the sample mean, like the Welch Overlapped Segment Averaging (WOSA) [1967]. The Schwock and Abadi [2021] Welch Percentile (SAWP) estimator avoids the loud transient bias by replacing the averaging of the WOSA with a scaled order statistic (OS) of the sample power spectrum. While the scaling ensures the SAWP is unbiased regardless of which OS is chosen, the 80th percentile minimizes the variance of the SAWP estimator in a scenario without loud transients. However, the 80th percentile SAWP is still vulnerable to bias and increased variance from more frequent loud transients. Also, the rate of occurrence of loud transients may change with time, requiring the SAWP to adapt which percentile is used. To approach this challenge, this talk proposes a Universal SAWP estimator which is a weighted sum across different fixed percentile SAWP estimators. At each iteration, the Universal SAWP updates the blend weights to promote the percentile with the lowest sample variance over recent observations. In computer simulations, the Universal SAWP quickly assigns higher weights to the lowest variance estimators as the occurrence of loud transients increases. Overall, the Universal SAWP achieves equal or lower variance as the best fixed percentile SAWP estimator. [Work supported by ONR Code 321US.]

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R, Saranya, and Anandakrishnan N. "Quantum Particle Swarm Optimization (QPSO) based Robust Estimation in Real-Time Ultrasound Strain Imaging." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 07, no. 02 (April 3, 2019): 01–10. http://dx.doi.org/10.9756/sijcsea/v7i2/06050150101.

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Kong, Xiangyu, Ying Chen, Tao Xu, Chengshan Wang, Chengsi Yong, Peng Li, and Li Yu. "A Hybrid State Estimator Based on SCADA and PMU Measurements for Medium Voltage Distribution System." Applied Sciences 8, no. 9 (September 1, 2018): 1527. http://dx.doi.org/10.3390/app8091527.

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With the increasing importance of renewable energy and flexible loads, the operation of the distribution system is becoming more stochastic and complex, and it is necessary to monitor the power system in real-time. Considering the gradual applications of intelligent electronic devices in the distribution systems, a hybrid state estimator based on supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) measurements is proposed in this paper, which consists of the improved robust estimation and linear state estimation. At the time of SCADA data acquisition, the improved robust estimation combining the SCADA measurements with PMU measurements is performed. To eliminate the effect of bad data, the internal student residual method is introduced, and the robust thresholds are adjusted adaptively. Then the linear state estimation is performed at the time of PMU data acquisition based on the results of the previous estimation time and the PMU measurements, which can quickly correct the robust estimation results and track the changes of the distribution system. Finally, the effectiveness and performance of the proposed method are verified in a modified IEEE 33-bus distribution system and a real distribution system in China.

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Li, Zhen, Zhaoqi Gao, Fengyuan Sun, Jinghuai Gao, and Wei Zhang. "Instantaneous Frequency Extraction for Nonstationary Signals via a Squeezing Operator with a Fixed-Point Iteration Method." Remote Sensing 16, no. 8 (April 16, 2024): 1412. http://dx.doi.org/10.3390/rs16081412.

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The instantaneous frequency (IF) is an important feature for the analysis of nonstationary signals. For IF estimation, the time–frequency representation (TFR)-based algorithm is used in a common class of methods. TFR-based methods always need the representation concentrated around the “true” IFs and the number of components within the signal. In this paper, we propose a novel method to adaptively estimate the IFs of nonstationary signals, even for weak components of the signals. The proposed technique is not based on the TFR: it is based on the frequency estimation operator (FEO), and the short-time Fourier transform (STFT) is used as its basis. As we know, the FRO is an exact estimation of the IF for weak frequency-modulated (FM) signals, but is not appropriate for strong FM modes. Through theoretical derivation, we determine that the fixed points of the FEOwith respect to the frequency are equivalent to the ridge of the STFT spectrum. Furthermore, the IF of the linear chirp signals is just the fixed points of the FEO. Therefore, we apply the fixed-point algorithm to the FEO to realize the precise and reliable estimation of the IF, even for highly FM signals. Finally, the results using synthetic and real signals show the utility of the proposed method for IF estimation and that it is more robust than the compared method. It should be noted that the proposed method employing the FEO only computes the first-order differential of the STFT for the chirp-like signals, while it can provide a result derived using the second-order estimation operator. Moreover, this new method is effective for the IF estimation of weak components within a signal.

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Shergei, M., and U. Shaked. "Robust H -estimation of nonlinear discrete-time processes." International Journal of Control 67, no. 4 (January 1997): 603–18. http://dx.doi.org/10.1080/002071797224108.

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Kulkarni, P. M., and C. C. Heyde. "Optimal robust estimation for discrete time stochastic processes." Stochastic Processes and their Applications 26 (1987): 267–76. http://dx.doi.org/10.1016/0304-4149(87)90180-3.

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Čížek, Pavel. "Efficient robust estimation of time-series regression models." Applications of Mathematics 53, no. 3 (June 2008): 267–79. http://dx.doi.org/10.1007/s10492-008-0009-x.

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Mirheidari, Saleh, Matthew Franchek, Karolos Grigoriadis, Javad Mohammadpour, Yue-Yun Wang, and Ibrahim Haskara. "Real-time and robust estimation of biodiesel blends." Fuel 92, no. 1 (February 2012): 37–48. http://dx.doi.org/10.1016/j.fuel.2011.06.060.

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Han, Cun Wu, De Hui Sun, and Song Bi. "Adaptive Control for Discrete-Time Systems with Time-Varying Delay." Applied Mechanics and Materials 556-562 (May 2014): 2289–92. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2289.

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This paper presents an adaptive robust controller for discrete-time systems with time-varying uncertainty and time-varying delay. The controller is designed based on the online parameter estimation and robust H∞ approach. Simulation result is given to verify the effectiveness of the proposed controller.

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Qiu, Yunxiu, Soo-Cheng Chuah, Ruhaini Muda, and Theng-Huey Goh. "The Impact of Foreign Direct Investment, Green Technology Innovation and GDP on CO2 Emissions in Western China: A Static Panel Data Analysis." Information Management and Business Review 16, no. 1(I)S (April 27, 2024): 204–15. http://dx.doi.org/10.22610/imbr.v16i1(i)s.3743.

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This study investigates the impact of FDI, green technology innovation and GDP on CO2 emissions in the western region of China. This study applies the extended STIRPAT model, selects the relevant data of nine provinces in western China from 2000 to 2019, applies the static panel data analysis method, compares and analyses the estimation methods of Pooled OLS, fixed effect and random effect, and finally adopts the robust standard error estimation method of fixed effect. The results of the robust standard error estimation indicate that, in addition to the negative impact of green technology innovation on CO2 emissions, CO2 emissions are positively impacted by FDI, GDP, population, and the proportion of the secondary and tertiary industries. Therefore, reasonable introduction of FDI and improving green technology innovation levels are crucial in reducing CO2 emissions in the western region of China.

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Emvalomatis, Grigorios, Spiro E. Stefanou, and Alfons Oude Lansink. "Estimation of Stochastic Frontier Models with Fixed Effects through Monte Carlo Maximum Likelihood." Journal of Probability and Statistics 2011 (2011): 1–13. http://dx.doi.org/10.1155/2011/568457.

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Estimation of nonlinear fixed-effects models is plagued by the incidental parameters problem. This paper proposes a procedure for choosing appropriate densities for integrating the incidental parameters from the likelihood function in a general context. The densities are based on priors that are updated using information from the data and are robust to possible correlation of the group-specific constant terms with the explanatory variables. Monte Carlo experiments are performed in the specific context of stochastic frontier models to examine and compare the sampling properties of the proposed estimator with those of the random-effects and correlated random-effects estimators. The results suggest that the estimator is unbiased even in short panels. An application to a cross-country panel of EU manufacturing industries is presented as well. The proposed estimator produces a distribution of efficiency scores suggesting that these industries are highly efficient, while the other estimators suggest much poorer performance.

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Liu, Shew Fan, and Zhenlin Yang. "Robust estimation and inference of spatial panel data models with fixed effects." Japanese Journal of Statistics and Data Science 3, no. 1 (April 18, 2020): 257–311. http://dx.doi.org/10.1007/s42081-020-00075-y.

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Čížek, P., and M. Aquaro. "Robust estimation and moment selection in dynamic fixed-effects panel data models." Computational Statistics 33, no. 2 (December 11, 2017): 675–708. http://dx.doi.org/10.1007/s00180-017-0782-7.

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Journal articles on the topic 'Fixed-Time and robust estimation'

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