Relevant bibliographies by topics / Recursive window estimation

Academic literature on the topic 'Recursive window estimation'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Recursive window estimation"

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Yu, Lei, Yong-li Zhang, Meng-di Yuan, Rui-qing Liu, and Qi Zhang. "Recursive Method in Modal Parameter Identification of Aerospace Structures under Non-Gaussian Noise." Shock and Vibration 2020 (June 5, 2020): 1–12. http://dx.doi.org/10.1155/2020/2946709.

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Operational modal parameter identification is a tough problem in aerospace engineering due to the complex mechanics environment, various noises, and limited computational resources. In this paper, a novel, recursive, robust, and high-efficiency modal parameter identification approach is proposed for this issue. The kernelized time-dependent autoregressive moving average (TARMA) model is adopted to model the nonstationary responses, a recursive estimator is established based on the maximum correntropy criterion, and sliding-window technique is applied to fix the computational complexity, which ensures the approach its estimation accuracy, robustness, and high efficiency. Finally, steps of the identification procedure and model selection are presented. An experimental scheme is proposed for validation, and the proposed approach is comparatively assessed against the classical recursive pseudo-linear regression TARMA method via Monte Carole tests of a time-varying experimental system. The results of the comparative study demonstrate that the proposed method achieves similar estimation accuracy and higher computation efficiency under the Gaussian environment. Moreover, a superior estimation accuracy and enhanced robustness are rendered under additive non-Gaussian impulsive noise.
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Gao, Wei, Jingchun Li, Guangtao Zhou, and Qian Li. "Adaptive Kalman Filtering with Recursive Noise Estimator for Integrated SINS/DVL Systems." Journal of Navigation 68, no. 1 (August 15, 2014): 142–61. http://dx.doi.org/10.1017/s0373463314000484.

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This paper considers the estimation of the process state and noise parameters when the statistics of the process and measurement noise are unknown or time varying in the integration system. An adaptive Kalman Filter (AKF) with a recursive noise estimator that is based on maximum a posteriori estimation and one-step smoothing filtering is proposed, and the AKF can provide accurate noise statistical parameters for the Kalman filter in real-time. An exponentially weighted fading memory method is introduced to increase the weights of the recent innovations when the noise statistics are time varying. Also, the innovation covariances within a moving window are averaged to correct the noise statistics estimator. Experiments on the integrated Strapdown Inertial Navigation System (SINS)/ Doppler Velocity Log (DVL) system show that the proposed AKF improves the estimation accuracy effectively and the AKF is robust in the presence of vigorous-manoeuvres and rough sea conditions.
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Amin, M. G. "A comparison between two measures of convergence in recursive-window based spectrum estimation." IEEE Transactions on Acoustics, Speech, and Signal Processing 38, no. 8 (1990): 1457–59. http://dx.doi.org/10.1109/29.57580.

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Gupta, Rangan, and Christian Pierdzioch. "Forecasting the Volatility of Crude Oil: The Role of Uncertainty and Spillovers." Energies 14, no. 14 (July 10, 2021): 4173. http://dx.doi.org/10.3390/en14144173.

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We use a dataset for the group of G7 countries and China to study the out-of-sample predictive value of uncertainty and its international spillovers for the realized variance of crude oil (West Texas Intermediate and Brent) over the sample period from 1996Q1 to 2020Q4. Using the Lasso estimator, we found evidence that uncertainty and international spillovers had predictive value for the realized variance at intermediate (two quarters) and long (one year) forecasting horizons in several of the forecasting models that we studied. This result holds also for upside (good) and downside (bad) variance, and irrespective of whether we used a recursive or a rolling estimation window. Our results have important implications for investors and policymakers.
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Wang, Song. "Windowed Least Square Algorithm Based PMSM Parameters Estimation." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/131268.

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Stator resistance and inductances ind-axis andq-axis of permanent magnet synchronous motors (PMSMs) are important parameters. Acquiring these accurate parameters is usually the fundamental part in driving and controlling system design, to guarantee the performance of driver and controller. In this paper, we adopt a novel windowed least algorithm (WLS) to estimate the parameters with fixed value or the parameter with time varying characteristic. The simulation results indicate that the WLS algorithm has a better performance in fixed parameters estimation and parameters with time varying characteristic identification than the recursive least square (RLS) and extended Kalman filter (EKF). It is suitable for engineering realization in embedded system due to its rapidity, less system resource possession, less computation, and flexibility to adjust the window size according to the practical applications.
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SHIN, JAEHYUN, YONGMIN ZHONG, JULIAN SMITH, and CHENGFAN GU. "ADAPTIVE UNSCENTED KALMAN FILTER FOR ONLINE SOFT TISSUES CHARACTERIZATION." Journal of Mechanics in Medicine and Biology 17, no. 07 (November 2017): 1740014. http://dx.doi.org/10.1142/s0219519417400140.

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Online soft tissue characterization is important for robotic-assisted minimally invasive surgery to achieve precise and stable robotic control with haptic feedback. This paper presents a new adaptive unscented Kalman filter based on the nonlinear Hunt–Crossley model for online soft tissue characterization without requiring the characteristics of system noise. This filter incorporates the concept of Sage windowing in the traditional unscented Kalman filter to adaptively estimate system noise covariance using predicted residuals within a time window. In order to account for the inherent relationship between the current and previous states of soft tissue deformation involved in robotic-assisted surgery and improve the estimation performance, a recursive estimation of system noise covariance is further constructed by introducing a fading scaling factor to control the contributions between noise covariance estimations at current and previous time points. The proposed adaptive unscented Kalman filter overcomes the limitation of the traditional unscented Kalman filter in requiring the characteristics of system noise. Simulations and comparisons show the efficacy of the suggested nonlinear adaptive unscented Kalman filter for online soft tissue characterization.
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Yang, Ruo-Nan, Wei-Tao Zhang, and Shun-Tian Lou. "Adaptive Blind Channel Estimation for MIMO-OFDM Systems Based on PARAFAC." Wireless Communications and Mobile Computing 2020 (October 24, 2020): 1–17. http://dx.doi.org/10.1155/2020/8396930.

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In order to track the changing channel in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, it is prior to estimate channel impulse response adaptively. In this paper, we proposed an adaptive blind channel estimation method based on parallel factor analysis (PARAFAC). We used an exponential window to weight the past observations; thus, the cost function can be constructed via a weighted least squares criterion. The minimization of the cost function is equivalent to the decomposition of third-order tensor which consists of the weighted OFDM data symbols. To reduce the computational load, we adopt a recursive singular value decomposition method for tensor decomposition; then, the channel parameters can be estimated adaptively. Simulation results validate the effectiveness of the proposed algorithm under diverse signalling conditions.
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Zhou, Zebo, Bofeng Li, and Yunzhong Shen. "A Window-Recursive Approach for GNSS Kinematic Navigation Using Pseudorange and Doppler Measurements." Journal of Navigation 66, no. 2 (November 20, 2012): 295–313. http://dx.doi.org/10.1017/s0373463312000549.

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In kinematic Global Navigation Satellite Systems (GNSS) navigation, the Kalman Filter (KF) solution relies, to a great extent, on the quality of the dynamic model that describes the moving object's motion behaviour. However, it is rather difficult to establish a precise dynamic model that only connects the previous state and the current state, since these high-order quantities are usually unavailable in GNSS navigation receivers. To overcome such limitations, the Window-Recursive Approach (WRA) that employs the previous multiple states to predict the current one was developed in Zhou et al., (2010). Its essence is to adaptively fit the moving object's motion behaviour using the multiple historical states in a short time span. Up to now, the WRA method has been performed only using GNSS pseudorange measurements. However, in GNSS navigation fields, the strength of pseudorange observation model is usually weak due to various reasons, e.g., multi-path delay, outliers, insufficient visible satellites. As an important complementary measurement, Doppler can be used to aid Position and Velocity (PV) estimation. In this contribution, implementation of WRA will be developed using the pseudorange and Doppler measurements. Its corresponding state transition matrix is constructed based on the Newton's Forward Difference Extrapolation (NFDE) and Definite Integral (DI) methods for the efficient computation. The new implementation of WRA is evaluated using the real kinematic vehicular GNSS data with two sampling rates. The results show that: (i)aided by GNSS Doppler measurement, the new implementation of WRA significantly improves the accuracy compared with the pseudorange-only WRA.(ii)In high sampling rate, the WRA works best in the case of 2 epochs in time window, while in the low sampling rate, it obtains better solutions if more epochs involved in time window.(iii)Compared with KF with constant velocity dynamic model, the WRA demonstrates better in the self-adaptation and validity.(iv)As a benefit of WRA itself, the NFDE/DI-based state transition matrix for WRA can be previously computed offline without increasing the computation burdens.
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Duzinkiewicz, Kazimierz, and Mietek A. Brdys. "SET MEMBERSHIP ESTIMATION OF PARAMETERS AND VARIABLES IN DYNAMIC NETWORKS BY RECURSIVE ALGORITHMS WITH MOVING MEASUREMENT WINDOW." IFAC Proceedings Volumes 38, no. 1 (2005): 21–26. http://dx.doi.org/10.3182/20050703-6-cz-1902.01544.

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Yang, Ruo-Nan, Wei-Tao Zhang, and Shun-Tian Lou. "Joint Adaptive Blind Channel Estimation and Data Detection for MIMO-OFDM Systems." Wireless Communications and Mobile Computing 2020 (July 2, 2020): 1–9. http://dx.doi.org/10.1155/2020/2508130.

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In order to track a changing channel in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, it is a priority to estimate channel impulse response adaptively. In this paper, we propose an adaptive blind channel estimation method based on parallel factor analysis (PARAFAC). We used an exponential window to weigh the past observations; thus, the cost function can be constructed via a weighted least squares criterion. The minimization of the cost function is equivalent to the decomposition of a third-order tensor, which consists of the weighted OFDM data symbols. By preserving the Khatri-Rao product, we used a recursive least squares solution to update the estimated subspace at each time instant, then the channel parameters can be estimated adaptively, and the algorithm achieves superior convergence performance. Simulation results validate the effectiveness of the proposed algorithm.
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Dissertations / Theses on the topic "Recursive window estimation"

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Martins, Carlos Henrique Nascimento. "Estudo e implementação de um analisador de harmônicos variantes no tempo." Universidade Federal de Juiz de Fora (UFJF), 2015. https://repositorio.ufjf.br/jspui/handle/ufjf/4176.

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CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Esta tese apresenta as etapas de desenvolvimento de um sistema de monitoramento de parâmentos de qualidade de energia dedicado a análise de harmônicos variantes no tempo através do equipamento denominado AHVT (Analisador de Harmônicos Variantes no Tempo). O desenvolvimento do trabalho é composto por: (i) estudo e implementação MATLAB de algoritmos para processamento em tempo real, com capacidade de sintonização dos componentes harmônicos; (ii) análise e desenvolvimento de estratégias para detecção e validação da presença de interharmônicos próximos à frequência fundamental e suas consequência na estimação de parâmetros como fase, amplitude e frequência para o componente fundamental, (iii) proposta de implementação do dispositivo, sistema de aquisição/ condicionamento de sinais/ filtragem, sistema de conversão analógico digital e plataforma de processamentoDSP/FPGA, sistema de transmissão de dados e plataformas de análise online/offline dos eventos de harmônicos variantes no tempo; (iv) plataforma de simulação do Analisador de Harmônicos Variantes no Tempo (AHVT) para estudo dos métodos de trigger para detecção e captura dos eventos.
In this work is presented the steps of development and implementation of a Power Quality paramaters monitoring system with main goal events denomined ”time arying harmonics”named of Time Varying Harmonic Analyzer. The development is comprises:(i) research and implementation of real time algorithms with capable to tuning harmonic waves,(ii) Analyze and research/development of strategies for detect and validation of interharmonics with frequencies near of fundamental, and conseguencies and challenges to phase, magnitude and frequency estimation with presence interharmonic waveform (iii) The proposal of a hardware design including analog to digital conversion and digital signal processing plataform, broadcast data link and IHM(Interface Human Machine) for online and offline analyzes to time varying harmonic analyzer;(iiii)off-line simulation plataform of Analisador de Harmônicos Variantes no Tempo Time Varying Harmonic Analyzer (TVHA) to trigger detect methods to detection and capture of waveforms.
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Evestedt, Magnus. "Parameter and State Estimation with Information-rich Signals." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis Acta Universitatis Upsaliensis, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8315.

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Book chapters on the topic "Recursive window estimation"

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Evensen, Geir, Femke C. Vossepoel, and Peter Jan van Leeuwen. "Problem Formulation." In Springer Textbooks in Earth Sciences, Geography and Environment, 9–26. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96709-3_2.

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AbstractThis chapter introduces the model-state- and parameter estimation problem from basic principles starting with Bayes’ theorem. We define the general problem formulation and introduce the concept of Bayes’ theorem solved recursively over a sequence of assimilation time windows.
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Zhu, Bonnie, and Shankar Sastry. "Intrusion Detection and Resilient Control for SCADA Systems." In Securing Critical Infrastructures and Critical Control Systems, 352–83. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2659-1.ch015.

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Designed without cyber security in mind, most existing Supervisory Control And Data Acquisition (SCADA) systems make it a big challenge to modify the conventional Information Technology (IT) intrusion detection techniques, both to counter the threat of cyber attacks due to their standardization and connectivity to the Internet, and to achieve resilient control without fully retrofitting. The author presents a taxonomy and a set of metrics of SCAD-specific intrusion detection techniques by heightening their possible use in addition to explaining the nuance associated with such task and enumerating Intrusion Detection Systems (IDS) that have been proposed to undertake this endeavor. She identifies the deficits and voids in current research and offers recommendations on which strategies are most likely to succeed, in part through presenting a prototype of her efforts towards this goal. Specifically, she introduces an early anomaly detection and resilient estimation scheme consisting of a robust online recursive algorithm, which is based on the Kalman Filter in a state space model setting. This online window limited Robust Generalized Likelihood Ratio Test (RGLRT) that the author proposes identifies and detects outliers among real-time multidimensional measurements of dynamical systems without any a priori knowledge of the occurrence time or distribution of the outliers. It attains a low detection delay and an optimal stopping time that yields low rates in false alarm and miss detection while maintaining the optimal online estimation performance under normal conditions. The author proposes a set of qualitative and quantitative metric to measure its optimality in the context of cyber-physical systems.
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Van Loi, Nguyen, Tran Quoc Tuan, Tran Trung Kien, Tran Van Truong, and Tran Vu Hop. "Performance Evaluation of Radar Range-Bearing Centroid Processing Using Time Series Analysis." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210201.

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The paper deals with a problem of performance evaluation of the range-bearing centroid processing for a surveillance radar. First, we review several techniques for centroid processing that are Moving window estimator, Beam shape centroid estimator, Center of mass correlation and Recursive least-squares centroid estimator. Then we point out that the range root-mean-square error (RMSE) and bearing RMSE are not sufficient for performance evaluation of the range-bearing centroid processing. Further, a new parameter using time series analysis for evaluation of structural stability of the centroid processing is proposed. As an illustration, a test with data from an X-band coastal surveillance radar is given.
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Conference papers on the topic "Recursive window estimation"

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Ledet, Jeffrey H., Vesselin P. Jilkov, and X. Rong Li. "Recursive Sliding-Window Algorithm for Constrained Multiple-Model MAP Estimation." In 2018 21st International Conference on Information Fusion (FUSION 2018). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455611.

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Hsieh, S. F., and K. J. R. Liu. "A residual-based selective window for robust recursive least squares estimation." In 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.150513.

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Chen Xianghua, Xu Ouguan, and Zou Hongbo. "Recursive PLS soft sensor with moving window for online PX concentration estimation in an industrial isomerization unit." In 2009 Chinese Control and Decision Conference (CCDC). IEEE, 2009. http://dx.doi.org/10.1109/ccdc.2009.5195246.

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Radhakrishnan, Gowtham, Bernt J. Leira, Zhen Gao, Svein Sævik, and Alojz Gomola. "Motion Response Prediction of Marine Vessels Based on Hydrodynamic Models Updated Through On-Site Measurements." In ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/omae2022-78912.

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Abstract Decision support systems in offshore vessels utilize wave parameters in combination with physics-based vessel models to predict the vessel behavior prior to the initiation and execution of a marine operation. These predictions are, usually, accompanied by significant uncertainties inherent in the estimation of wave statistical parameters, idealized parametric spectra, and system variables. Consequently, the predictions may deviate considerably from the real behavior of the vessel. Therefore, this study uses numerical wave spectra corresponding to a site in the North Sea in conjunction with a hydrodynamic model adapted to measurements to make more accurate intermediate-term response predictions. Considering a weather-restricted marine operation, the intermediate-term predictions involve simulating the responses for any time window within the upcoming 72 hours. The vessel model’s uncertainty is minimized by calibrating the influential parameters utilizing the full-scale response measurements within an optimization framework. The subsequent Roll predictions based on calibrated parameters exhibit better alignment with the measured Roll motions. The application of recursive optimization showed a significant reduction in prediction errors in an actual marine operation.
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Dumortier, Baldwin, Emmanuel Vincent, and Madalina Deaconu. "Recursive Bayesian estimation of the acoustic noise emitted by wind farms." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952217.

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Kaviri, Sajjad M., T. A. Najafabadi, B. Mohammadpour, Praveen Jain, and Alireza Bakhshai. "Modified window, recursive least square estimator for active and reactive powers in single-phase AC systems." In 2016 IEEE 7th International Symposium on Power Electronics for Distributed Generation Systems (PEDG). IEEE, 2016. http://dx.doi.org/10.1109/pedg.2016.7527037.

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Qi, Xiaoke, Yu Li, and Haining Huang. "A combined recursive least square and least mean square equalization scheme based on windowed error autocorrelation estimation." In 2013 6th International Congress on Image and Signal Processing (CISP). IEEE, 2013. http://dx.doi.org/10.1109/cisp.2013.6743905.

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