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Thematische Bibliographien / Estimation of the process noise / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Estimation of the process noise“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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1

Gillard, Nicolas, Étienne Belin, and François Chapeau-Blondeau. "Stochastic Resonance with Unital Quantum Noise." Fluctuation and Noise Letters 18, no. 03 (2019): 1950015. http://dx.doi.org/10.1142/s0219477519500159.

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The fundamental quantum information processing task of estimating the phase of a qubit is considered. Following quantum measurement, the estimation efficiency is evaluated by the classical Fisher information which determines the best performance limiting any estimator and achievable by the maximum likelihood estimator. The estimation process is analyzed in the presence of decoherence represented by essential quantum noises that can affect the qubit and belonging to the broad class of unital quantum noises. Such a class especially contains the bit-flip, the phase-flip, the depolarizing noises,

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Wang, Chen, Yao-Wu Shi, Lan-Xiang Zhu, Li-Fei Deng, Yi-Ran Shi, and De-Min Wang. "Auto-regressive moving average parameter estimation for 1/f process under colored Gaussian noise background." Journal of Algorithms & Computational Technology 13 (January 2019): 174830261986743. http://dx.doi.org/10.1177/1748302619867439.

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Current algorithms for estimating auto-regressive moving average parameters of transistor 1/f process are usually under noiseless background. Transistor noises are measured by a non-destructive cross-spectrum measurement technique, with transistor noise first passing through dual-channel ultra-low noise amplifiers, then inputting the weak signals into data acquisition card. The data acquisition card collects the voltage signals and outputs the amplified noise for further analysis. According to our studies, the output transistor 1/f noise can be characterized more accurately as non-Gaussian α-s

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Shin, Vladimir, Rebbecca T. Y. Thien, and Yoonsoo Kim. "Receding Horizon Least Squares Estimator with Application to Estimation of Process and Measurement Noise Covariances." Mathematical Problems in Engineering 2018 (November 19, 2018): 1–15. http://dx.doi.org/10.1155/2018/5303694.

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This paper presents a noise covariance estimation method for dynamical models with rectangular noise gain matrices. A novel receding horizon least squares criterion to achieve high estimation accuracy and stability under environmental uncertainties and experimental errors is proposed. The solution to the optimization problem for the proposed criterion gives equations for a novel covariance estimator. The estimator uses a set of recent information with appropriately chosen horizon conditions. Of special interest is a constant rectangular noise gain matrices for which the key theoretical results

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Moon, Todd K., and Jacob H. Gunther. "Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates." Entropy 22, no. 5 (2020): 572. http://dx.doi.org/10.3390/e22050572.

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Estimating the parameters of the autoregressive (AR) random process is a problem that has been well-studied. In many applications, only noisy measurements of AR process are available. The effect of the additive noise is that the system can be modeled as an AR model with colored noise, even when the measurement noise is white, where the correlation matrix depends on the AR parameters. Because of the correlation, it is expedient to compute using multiple stacked observations. Performing a weighted least-squares estimation of the AR parameters using an inverse covariance weighting can provide sig

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Mageed Hag Elamin, Khalid Abd El. "Particle Filtering for Enhanced Parameter Estimation in Bilinear Systems Under Colored Noise." Current Research in Statistics & Mathematics 3, no. 3 (2024): 01–20. http://dx.doi.org/10.33140/crsm.03.03.01.

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This paper addresses the challenging problem of parameter estimation in bilinear systems under colored noise. A novel approach, termed B-PF-RLS, is proposed, combining a particle filter (PF) with a recursive least squares (RLS) estimator. The B-PF-RLS algorithm tackles the complexities arising from system nonlinearities and colored noise by effectively estimating unknown system states using the particle filter, which are then integrated into the RLS parameter estimation process. Furthermore, the paper introduces an enhanced particle filter that eliminates the need for explicit knowledge of the

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Tan, Hanlin, Huaxin Xiao, Shiming Lai, Yu Liu, and Maojun Zhang. "Pixelwise Estimation of Signal-Dependent Image Noise Using Deep Residual Learning." Computational Intelligence and Neuroscience 2019 (September 9, 2019): 1–12. http://dx.doi.org/10.1155/2019/4970508.

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In traditional image denoising, noise level is an important scalar parameter which decides how much the input noisy image should be smoothed. Existing noise estimation methods often assume that the noise level is constant at every pixel. However, real-world noise is signal dependent, or the noise level is not constant over the whole image. In this paper, we attempt to estimate the precise and pixelwise noise level instead of a simple global scalar. To the best of our knowledge, this is the first work on the problem. Particularly, we propose a deep convolutional neural network named “deep resid

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Liang, Xu, and Chenglin Wen. "Sequential Fusion Least Squares Method for State Estimation of Multi-Sensor Linear Systems Under Noise Cross-Correlation." Symmetry 17, no. 6 (2025): 948. https://doi.org/10.3390/sym17060948.

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This paper investigates a multi-sensor system for the state estimation of a maneuvering target, wherein the process noise of the target dynamics and the measurement noise of the sensor network are mutually correlated, and the measurement noises across different sensors are also cross-correlated. Under such conditions, we propose a globally optimal sequential least squares fusion estimation algorithm within the framework of linear minimum mean square error (LMMSE) estimation. This method is specifically designed to preserve structural symmetry and to accommodate the time-ordered arrival of sens

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Ali, Suhad A., C. Elaf A. Abbood, and Shaymaa Abdu LKadhm. "Salt and Pepper Noise Removal Using Resizable Window and Gaussian Estimation Function." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 5 (2016): 2219. http://dx.doi.org/10.11591/ijece.v6i5.11641.

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<p class="Default">Most types of the images are corrupted in many ways that because exposed to different types of noises. The corruptions happen during transmission from space to another, during storing or capturing. Image processing has various techniques to process the image. Before process the image, there is need to remove noise that corrupt the image and enhance it to be as near as to the original image. This paper proposed a new method to process a particular common type of noise. This method removes salt and pepper noise by using many techniques. First, detect the noisy pixel, the

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Ali, Suhad A., C. Elaf A. Abbood, and Shaymaa Abdu LKadhm. "Salt and Pepper Noise Removal Using Resizable Window and Gaussian Estimation Function." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 5 (2016): 2219. http://dx.doi.org/10.11591/ijece.v6i5.pp2219-2224.

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<p class="Default">Most types of the images are corrupted in many ways that because exposed to different types of noises. The corruptions happen during transmission from space to another, during storing or capturing. Image processing has various techniques to process the image. Before process the image, there is need to remove noise that corrupt the image and enhance it to be as near as to the original image. This paper proposed a new method to process a particular common type of noise. This method removes salt and pepper noise by using many techniques. First, detect the noisy pixel, the

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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 (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 varyin

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Jwo, Dah-Jing, and Chun-Fan Pai. "Incorporation of Neural Network State Estimator for GPS Attitude Determination." Journal of Navigation 57, no. 1 (2004): 117–34. http://dx.doi.org/10.1017/s0373463303002625.

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The Global Positioning System (GPS) can be employed as a free attitude determination interferometer when carrier phase measurements are utilized. Conventional approaches for the baseline vectors are essentially based on the least-squares or Kalman filtering methods. The raw attitude solutions are inherently noisy if the solutions of baseline vectors are obtained based on the least-squares method. The Kalman filter attempts to minimize the error variance of the estimation errors and will provide the optimal result while it is required that the complete a priori knowledge of both the process noi

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Musunuri, Yogendra Rao, and Oh-Seol Kwon. "State Estimation Using a Randomized Unscented Kalman Filter for 3D Skeleton Posture." Electronics 10, no. 8 (2021): 971. http://dx.doi.org/10.3390/electronics10080971.

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In this study, we propose a method for minimizing the noise of Kinect sensors for 3D skeleton estimation. Notably, it is difficult to effectively remove nonlinear noise when estimating 3D skeleton posture; however, the proposed randomized unscented Kalman filter reduces the nonlinear temporal noise effectively through the state estimation process. The 3D skeleton data can then be estimated at each step by iteratively passing the posterior state during the propagation and updating process. Ultimately, the performance of the proposed method for 3D skeleton estimation is observed to be superior t

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Tan, Liguo, Yibo Wang, Changqing Hu, Xinbin Zhang, Liyi Li, and Haoxiang Su. "Sequential Fusion Filter for State Estimation of Nonlinear Multi-Sensor Systems with Cross-Correlated Noise and Packet Dropout Compensation." Sensors 23, no. 10 (2023): 4687. http://dx.doi.org/10.3390/s23104687.

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This paper is concerned with the problem of state estimation for nonlinear multi-sensor systems with cross-correlated noise and packet loss compensation. In this case, the cross-correlated noise is modeled by the synchronous correlation of the observation noise of each sensor, and the observation noise of each sensor is correlated with the process noise at the previous moment. Meanwhile, in the process of state estimation, since the measurement data may be transmitted in an unreliable network, data packet dropout will inevitably occur, leading to a reduction in estimation accuracy. To address

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Balakina, N., and A. Balakin. "Automation of the Process of Measuring and Evaluating Intermittent Industrial Noise." Bulletin of Science and Practice 7, no. 4 (2021): 231–35. http://dx.doi.org/10.33619/2414-2948/65/25.

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The estimation of intermittent industrial noise is considered, and one of the options for optimizing and automating the process of measuring industrial noise is proposed. A generalized block diagram of a noise dosimeter and a scheme of a measuring complex for dose estimation of noise using several microphones are presented.

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Bianchi, Federico, Simone Formentin, and Luigi Piroddi. "Process noise covariance estimation via stochastic approximation." International Journal of Adaptive Control and Signal Processing 34, no. 1 (2019): 63–76. http://dx.doi.org/10.1002/acs.3068.

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Dong, Lingyan, Hongli Xu, Xisheng Feng, Xiaojun Han, and Chuang Yu. "An Adaptive Target Tracking Algorithm Based on EKF for AUV with Unknown Non-Gaussian Process Noise." Applied Sciences 10, no. 10 (2020): 3413. http://dx.doi.org/10.3390/app10103413.

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An adaptive target tracking method based on extended Kalman filter (TT-EKF) is proposed to simultaneously estimate the state of an Autonomous Underwater Vehicle (AUV) and an mobile recovery system (MRS) with unknown non-Gaussian process noise in homing process. In the application scenario of this article, the process noise includes the measurement noise of AUV heading and forward speed and the estimation error of MRS heading and forward speed. The accuracy of process noise covariance matrix (PNCM) can affect the state estimation performance of the TT-EKF. The variational Bayesian based algorit

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Yalçın, Bulut, and Ünal Barış. "Process Noise Source Localization Using Kalman Filter." Journal of Scientific, Technology and Engineering Research 1, no. 2 (2020): 19–24. https://doi.org/10.5281/zenodo.4048219.

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<em>Due to complexity in the systems, spatial distribution of unmeasured processnoise that is required for the controller and observer design are often unknown. In this study an innovations correlations approach developed in Kalman Filter theory is used to localize the process noise from output measurements. The approach calculates covariance matrices from analysis of resulting innovations from an arbitrary filter gain. Aim of this paper is to review the innovation correlations approach and to evaluate its performance for localization of the process noise.Numerical results suggest that the met

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Vorobeychikov, Sergey E., and Yulia B. Burkatovskaya. "Non-asymptotic Confidence Estimation of the Autoregressive Parameter in AR(1) Process with an Unknown Noise Variance." Austrian Journal of Statistics 49, no. 4 (2020): 19–26. http://dx.doi.org/10.17713/ajs.v49i4.1121.

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The paper considers the estimation problem of the autoregressive parameter in the first-order autoregressive process with Gaussian noises when the noise variance is unknown. We propose a non-asymptotic technique to compensate the unknown variance, and then, to construct a point estimator with any prescribed mean square accuracy. Also a fixed-width confidence interval with any prescribed coverage accuracy is proposed. The results of Monte-Carlo simulations are given.

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Chuang, Chia-Hua, and Chun-Liang Lin. "On Robust State Estimation of Gene Networks." Biomedical Engineering and Computational Biology 2 (January 2010): 117959721000200. http://dx.doi.org/10.1177/117959721000200001.

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Gene networks in biological systems are not only nonlinear but also stochastic due to noise corruption. How to accurately estimate the internal states of the noisy gene networks is an attractive issue to researchers. However, the internal states of biological systems are mostly inaccessible by direct measurement. This paper intends to develop a robust extended Kalman filter for state and parameter estimation of a class of gene network systems with uncertain process noises. Quantitative analysis of the estimation performance is conducted and some representative examples are provided for demonst

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Chen, Yuan, and Xiaohe Huang. "Second-Order Central Difference Particle Filter Algorithm for State of Charge Estimation in Lithium-Ion Batteries." World Electric Vehicle Journal 15, no. 4 (2024): 152. http://dx.doi.org/10.3390/wevj15040152.

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The estimation of the state of charge (SOC) in lithium-ion batteries is a crucial aspect of battery management systems, serving as a key indicator of the remaining available capacity. However, the inherent process and measurement noises created during battery operation pose significant challenges to the accuracy of SOC estimation. These noises can lead to inaccuracies and uncertainties in assessing the battery’s condition, potentially affecting its overall performance and lifespan. To address this problem, we propose a second-order central difference particle filter (SCDPF) method. This method

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Safarinejadian, Behrouz, Nasrin Kianpour, and Mojtaba Asad. "State estimation in fractional-order systems with coloured measurement noise." Transactions of the Institute of Measurement and Control 40, no. 6 (2017): 1819–35. http://dx.doi.org/10.1177/0142331217691219.

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This paper presents new estimation methods for discrete fractional-order state-space systems with coloured measurement noise. A novel approach is proposed to convert a fractional system with coloured measurement noise to a system with white measurement noise in which the process and measurement noises are correlated with each other. In this paper, two new Kalman filter algorithms for fractional-order linear state-space systems with coloured measurement noise, as well as a new extended Kalman filter algorithm for state estimation in nonlinear fractional-order state-space systems with coloured m

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Feng, Bo, Mengyin Fu, Hongbin Ma, Yuanqing Xia, and Bo Wang. "Kalman Filter With Recursive Covariance Estimation—Sequentially Estimating Process Noise Covariance." IEEE Transactions on Industrial Electronics 61, no. 11 (2014): 6253–63. http://dx.doi.org/10.1109/tie.2014.2301756.

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23

Caballero-Águila, R., I. García-Garrido, and J. Linares-Pérez. "Optimal Fusion Filtering in Multisensor Stochastic Systems with Missing Measurements and Correlated Noises." Mathematical Problems in Engineering 2013 (2013): 1–14. http://dx.doi.org/10.1155/2013/418678.

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The optimal least-squares linear estimation problem is addressed for a class of discrete-time multisensor linear stochastic systems with missing measurements and autocorrelated and cross-correlated noises. The stochastic uncertainties in the measurements coming from each sensor (missing measurements) are described by scalar random variables with arbitrary discrete probability distribution over the interval[0,1]; hence, at each single sensor the information might be partially missed and the different sensors may have different missing probabilities. The noise correlation assumptions considered

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Wu, Nan, Lei Chen, Yongjun Lei, and Fankun Meng. "Adaptive estimation algorithm of boost-phase trajectory using binary asynchronous observation." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 230, no. 14 (2016): 2661–72. http://dx.doi.org/10.1177/0954410016630000.

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A kind of adaptive filter algorithm based on the estimation of the unknown input is proposed for studying the adaptive adjustment of process noise variance of boost phase trajectory. Polynomial model is used as the motion model of the boost trajectory, truncation error is regarded as an equivalent to the process noise and the unknown input and process noise variance matrix is constructed from the estimation value of unknown input according to the quantitative relationship among the unknown input, the state estimation error, and optimal process noise variance. The simulation results show that i

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Liu, Shing-Hong, Cheng-Hsiung Hsieh, Wenxi Chen, and Tan-Hsu Tan. "ECG Noise Cancellation Based on Grey Spectral Noise Estimation." Sensors 19, no. 4 (2019): 798. http://dx.doi.org/10.3390/s19040798.

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In recent years, wearable devices have been popularly applied in the health care field. The electrocardiogram (ECG) is the most used signal. However, the ECG is measured under a body-motion condition, which is easily coupled with some noise, like as power line noise (PLn) and electromyogram (EMG). This paper presents a grey spectral noise cancellation (GSNC) scheme for electrocardiogram (ECG) signals where two-stage discrimination is employed with the empirical mode decomposition (EMD), the ensemble empirical mode decomposition (EEMD) and the grey spectral noise estimation (GSNE). In the first

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PATEL, HIREN G., and SHAMBHU N. SHARMA. "SOME EVOLUTION EQUATIONS FOR AN ORNSTEIN–UHLENBECK PROCESS-DRIVEN DYNAMICAL SYSTEM." Fluctuation and Noise Letters 11, no. 04 (2012): 1250020. http://dx.doi.org/10.1142/s0219477512500204.

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The statistical properties of the Ornstein–Uhlenbeck (OU) process, a colored noise process, confirm the real noise statistics, since the real noise process has finite, nonzero correlation time. For this reason, it seems worthwhile to develop the estimation-theoretic scenarios of dynamical systems embedded in the colored noise environment as well. Importantly, the application of the Itô theory is not straightforward to the dynamical system in which the OU variable is a driving input. The augmented solution vector approach coupled with the Itô stochastic differential rule plays the pivotal role

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Kumar, R. Suresh, and P. Manimegalai. "Detection and Separation of Eeg Artifacts Using Wavelet Transform." International Journal of Informatics and Communication Technology (IJ-ICT) 7, no. 3 (2018): 149. http://dx.doi.org/10.11591/ijict.v7i3.pp149-156.

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Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-i

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R., Suresh Kumar, and P.Manimegalai. "Detection and Separation of Eeg Artifacts Using Wavelet Transform." International Journal of Informatics and Communication Technology (IJ-ICT) 7, no. 3 (2018): 127–34. https://doi.org/10.11591/ijict.v7i3.pp127-134.

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Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-i

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YE, ZHIPIN, and CHUANGYIN DANG. "PARAMETER ESTIMATION FOR LINEAR FRACTIONAL STABLE NOISE PROCESS." Journal of Circuits, Systems and Computers 14, no. 02 (2005): 233–47. http://dx.doi.org/10.1142/s0218126605002246.

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Over the past few years, scaling phenomena involving self-similarity and heavy-tailed distributions have attracted the interest of various researchers in telecommunications and networks. In this paper, we study the linear fractional stable noise (LFSN) which exhibits both long-range dependence and heavy tails property. LFSN can be represented as a linear process with weight coefficients and α-stable random variables. The coefficients of the linear process are determined by a kernel function and depend on five parameters. This paper focuses on estimating two unknown parameters a and b. Based on

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Liu, Shi Lin, and Zheng Pei. "Voice Activity Based on Noise Estimation in Noisy Environments." Applied Mechanics and Materials 239-240 (December 2012): 409–14. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.409.

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An improved project based on decision trees in noisy environments is proposed for robust endpoints detection. Firstly, the noise level of the environment is estimated by wavelet decomposition, and then whether the denoising process is done according to the noise level is determined. Next, the thresholds are obtained by decision trees for the signal. Finally, endpoints are detected by the double thresholds on different importance of the energy and zero-crossing rate (ZCR) in the corresponding situation. The simulation results indicate that the proposed method based on noise estimation can obtai

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Selvaraj, Poovarasan, and E. Chandra. "Ideal ratio mask estimation using supervised DNN approach for target speech signal enhancement." Journal of Intelligent & Fuzzy Systems 42, no. 3 (2022): 1869–83. http://dx.doi.org/10.3233/jifs-211236.

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The most challenging process in recent Speech Enhancement (SE) systems is to exclude the non-stationary noises and additive white Gaussian noise in real-time applications. Several SE techniques suggested were not successful in real-time scenarios to eliminate noises in the speech signals due to the high utilization of resources. So, a Sliding Window Empirical Mode Decomposition including a Variant of Variational Model Decomposition and Hurst (SWEMD-VVMDH) technique was developed for minimizing the difficulty in real-time applications. But this is the statistical framework that takes a long tim

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Jalil, Bushra, Zunera Jalil, Eric Fauvet, and Olivier Laligant. "Edge-Preserving Image Denoising Based on Lipschitz Estimation." Applied Sciences 11, no. 11 (2021): 5126. http://dx.doi.org/10.3390/app11115126.

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The information transmitted in the form of signals or images is often corrupted with noise. These noise elements can occur due to the relative motion, noisy channels, error in measurements, and environmental conditions (rain, fog, change in illumination, etc.) and result in the degradation of images acquired by a camera. In this paper, we address these issues, focusing mainly on the edges that correspond to the abrupt changes in the signal or images. Preserving these important structures, such as edges or transitions and textures, has significant theoretical importance. These image structures

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Mohamed, M., and N. Joy. "Lateral directional aircraft aerodynamic parameter estimation using adaptive stochastic nonlinear filter." Aeronautical Journal 125, no. 1294 (2021): 2217–28. http://dx.doi.org/10.1017/aer.2021.61.

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AbstractThis paper aims to accurately estimate the lateral directional aerodynamic parameters in real time irrespective of the variations in the process and measurement covariance matrices. The proposed algorithm for parameter estimation is based on the integration of adaptive techniques into a stochastic nonlinear filter. The proposed adaptive estimation algorithm is applied to flight test data, and the lateral directional derivatives are estimated in real time. The estimates are compared with those obtained from the Filter Error Method (FEM), an offline parameter estimation method accounting

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Wang, Chen, Yao-Wu Shi, Lan-Xiang Zhu, Li-Fei Deng, Yi-Ran Shi та De-Min Wang. "α-spectrum estimation for 1/f processes in noisy environments". Noise & Vibration Worldwide 50, № 2 (2019): 46–55. http://dx.doi.org/10.1177/0957456519827937.

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In the past, 1/ f noise was regarded as a stochastic process that accords with Gaussian distribution. According to our studies, the output transistor 1/ f noise can be characterized more accurately as non-Gaussian α-stable distribution rather than Gaussian distribution. We define and consistently estimate the samples normalized cross-correlations of linear S αS processes and propose a samples normalized cross-correlations–based α-spectrum method effective in noisy environments. Simulation results and diodes noise spectrum estimation results exhibit good performance.

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Macias, Michal, and Dominik Sierociuk. "Finite Length Triple Estimation Algorithm and its Application to Gyroscope MEMS Noise Identification." Acta Mechanica et Automatica 17, no. 2 (2023): 219–29. http://dx.doi.org/10.2478/ama-2023-0025.

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Abstract The noises associated with MEMS measurements can significantly impact their accuracy. The noises characterised by random walk and bias instability errors strictly depend on temperature effects that are difficult to specify during direct measurements. Therefore, the paper aims to estimate the fractional noise dynamics of the stationary MEMS gyroscope based on finite length triple estimation algorithm (FLTEA). The paper deals with the state, order and parameter estimation of fractional order noises originating from the MEMS gyroscope, being part of the popular Inertial Measurement Unit

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Niu, S., and D. G. Fisher. "Simultaneous estimation of process parameters, noise variance, and signal-to-noise ratio." IEEE Transactions on Signal Processing 43, no. 7 (1995): 1725–28. http://dx.doi.org/10.1109/78.398737.

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GRECKSCH, WILFRIED, and CONSTANTIN TUDOR. "A FILTERING PROBLEM FOR A LINEAR STOCHASTIC EVOLUTION EQUATION DRIVEN BY A FRACTIONAL BROWNIAN MOTION." Stochastics and Dynamics 08, no. 03 (2008): 397–412. http://dx.doi.org/10.1142/s021949370800238x.

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A linear unbiased and square mean optimal estimation is obtained for the mild solution process of a stochastic evolution equation with an infinite-dimensional fractional Brownian motion as noise and the noise in the observation process is a finite-dimensional Brownian motion. An innovation process is introduced and the estimation is obtained as a solution of a stochastic differential equation with a finite-dimensional noise. By using an approach based on the equivalence with a deterministic control problem, the estimation for the Fourier coefficients of the signal process is also determined.

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Locatelli, Fabiano, Konstantinos Christodoulopoulos, Michela Svaluto-Moreolo, Josep M. Fàbrega, and Salvatore Spadaro. "Machine Learning-Based in-band OSNR Estimation from Optical Spectra." IEEE Photonics Technology 31, no. 24 (2019): 1929–32. https://doi.org/10.1109/LPT.2019.2950058.

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Measuring the optical signal to noise ratio (OSNR) at certain network points is essential for failure handling, for single connection but also global network optimization. Estimating OSNR is inherently difficult in dense wavelength routed networks, where connections accumulate noise over different paths and tight filters do not allow the observation of the noise level at signal sides. We propose an in-band OSNR estimation process, which relies on a machine learning (ML) method, in particular on Gaussian process (GP) or support vector machine (SVM) regression. We acquired high-resolution optica

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Cheng, Yan, Shengkang Zhang, Xueyun Wang, and Haifeng Wang. "Self-Tuning Process Noise in Variational Bayesian Adaptive Kalman Filter for Target Tracking." Electronics 12, no. 18 (2023): 3887. http://dx.doi.org/10.3390/electronics12183887.

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Many practical systems, such as target tracking, navigation systems, autonomous vehicles, and other applications, are usually applied in dynamic conditions. Thus, the actual noise statistics characteristics of these systems are generally time varying and unknown, which will deteriorate the state estimation accuracy of the Kalman filter (KF) and even cause filter diverging. To address this issue, this paper proposes an adaptive process noise covariance (Qk)-based variational Bayesian adaptive Kalman filter (AQ-VBAKF) algorithm. Firstly, the adaptive factor is introduced to self-tune the process

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Asem, Khmag, Ghoul Sami, Abdul Rahman Al-Haddad Syed, and Kamarudin Noraziahtulhidayu. "Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 2 (2018): 915–24. https://doi.org/10.12928/TELKOMNIKA.v16i2.9060.

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In this paper, an automatic estimation of additive white Gaussian noise technique is proposed. This technique is built according to the local statistics of Gaussian noise. In the field of digital signal processing, estimation of the noise is considered as pivotal process that many signal processing tasks relies on. The main aim of this paper is to design a patch-based estimation technique in order to estimate the noise level in natural images and use it in blind image removal technique. The estimation processes is utilized selected patches which is most contaminated sub-pixels in the tested im

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Cui, Ge. "Application of Addition and Multiplication Noise Model Parameter Estimation in INSAR Image Processing." Mathematical Problems in Engineering 2022 (May 19, 2022): 1–10. http://dx.doi.org/10.1155/2022/3164513.

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INSAR images are inevitably contaminated by noise during the process of generation, transmission, compression, and reception. Noise not only affects the quality of the INSAR image, but also affects subsequent operations such as the design of corresponding filters, INSAR image segmentation, compression, restoration, and feature recognition. The INSAR image noise model is mainly divided into additive noise and multiplicative noise. Compared with additive noise, multiplicative noise is more complicated due to INSAR image correlation and non-Gaussian. Based on least squares algorithm system of add

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Pardede, Hilman, Kalamullah Ramli, Yohan Suryanto, Nur Hayati, and Alfan Presekal. "Speech Enhancement for Secure Communication Using Coupled Spectral Subtraction and Wiener Filter." Electronics 8, no. 8 (2019): 897. http://dx.doi.org/10.3390/electronics8080897.

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The encryption process for secure voice communication may degrade the speech quality when it is applied to the speech signals before encoding them through a conventional communication system such as GSM or radio trunking. This is because the encryption process usually includes a randomization of the speech signals, and hence, when the speech is decrypted, it may perceptibly be distorted, so satisfactory speech quality for communication is not achieved. To deal with this, we could apply a speech enhancement method to improve the quality of decrypted speech. However, many speech enhancement meth

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Fraanje, Rufus, René Beltman, Fidelis Theinert, Michiel van Osch, Teade Punter, and John Bolte. "Sensor Fusion of Odometer, Compass and Beacon Distance for Mobile Robots." International Journal of Artificial Intelligence and Machine Learning 10, no. 1 (2020): 1–17. http://dx.doi.org/10.4018/ijaiml.2020010101.

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The estimation of the pose of a differential drive mobile robot from noisy odometer, compass, and beacon distance measurements is studied. The estimation problem, which is a state estimation problem with unknown input, is reformulated into a state estimation problem with known input and a process noise term. A heuristic sensor fusion algorithm solving this state-estimation problem is proposed and compared with the extended Kalman filter solution and the Particle Filter solution in a simulation experiment.

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Hou, Pengyu, Jiuping Zha, Teng Liu, and Baocheng Zhang. "LS-VCE Applied to Stochastic Modeling of GNSS Observation Noise and Process Noise." Remote Sensing 14, no. 2 (2022): 258. http://dx.doi.org/10.3390/rs14020258.

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Stochastic models play a crucial role in global navigation satellite systems (GNSS) data processing. Many studies contribute to the stochastic modeling of GNSS observation noise, whereas few studies focus on the stochastic modeling of process noise. This paper proposes a method that is able to jointly estimate the variances of observation noise and process noise. The method is flexible since it is based on the least-squares variance component estimation (LS-VCE), enabling users to estimate the variance components that they are specifically interested in. We apply the proposed method to estimat

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Jiang, Haonan, and Yuanli Cai. "Adaptive Fifth-Degree Cubature Information Filter for Multi-Sensor Bearings-Only Tracking." Sensors 18, no. 10 (2018): 3241. http://dx.doi.org/10.3390/s18103241.

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Standard Bayesian filtering algorithms only work well when the statistical properties of system noises are exactly known. However, this assumption is not always plausible in real target tracking applications. In this paper, we present a new estimation approach named adaptive fifth-degree cubature information filter (AFCIF) for multi-sensor bearings-only tracking (BOT) under the condition that the process noise follows zero-mean Gaussian distribution with unknown covariance. The novel algorithm is based on the fifth-degree cubature Kalman filter and it is constructed within the information filt

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Wang, Dapeng, Hai Zhang, and Baoshuang Ge. "Adaptive Unscented Kalman Filter for Target Tacking with Time-Varying Noise Covariance Based on Multi-Sensor Information Fusion." Sensors 21, no. 17 (2021): 5808. http://dx.doi.org/10.3390/s21175808.

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In this paper, an innovative optimal information fusion methodology based on adaptive and robust unscented Kalman filter (UKF) for multi-sensor nonlinear stochastic systems is proposed. Based on the linear minimum variance criterion, this multi-sensor information fusion method has a two-layer architecture: at the first layer, a new adaptive UKF scheme for the time-varying noise covariance is developed and serves as a local filter to improve the adaptability together with the estimated measurement noise covariance by applying the redundant measurement noise covariance estimation, which is isola

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Gao, Zhaohui, Hua Zong, Yongmin Zhong, and Guangle Gao. "Limited Memory-Based Random-Weighted Kalman Filter." Sensors 24, no. 12 (2024): 3850. http://dx.doi.org/10.3390/s24123850.

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The Kalman filter is an important technique for system state estimation. It requires the exact knowledge of system noise statistics to achieve optimal state estimation. However, in practice, this knowledge is often unknown or inaccurate due to uncertainties and disturbances involved in the dynamic environment, leading to degraded or even divergent filtering solutions. To address this issue, this paper presents a new method by combining the random weighting concept with the limited memory technique to accurately estimate system noise statistics. To avoid the influence of excessive historical in

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Liu, Fang, Keyu Li, Wei Ge Liang, and Fu Qing Tian. "EKF with Measurement Noise Estimation Based on Wavelet Transform and Application for Target Tracking." Applied Mechanics and Materials 519-520 (February 2014): 1061–64. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.1061.

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The measurement noise variance in the process of EKF is prone to bring error accumulation and can lead to filter divergence. Aiming at this kind of shortcoming, in this paper we build model of target motion observed on a single measurement point in a two-dimensional plane firstly. Secondly, we compare two methods, the variance estimation based on the signal-to-noise separation of wavelet transform and EKF algorithm based on noise variance estimation, applying in target tracking. Then, we adopt the wavelet transform to distinguish noise from the measurement signal real-timely. And the median va

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Poborchaya, N. E., S. A. Zharkikh, and E. M. Lobov. "The method of moments in the problem of estimating the parameters of a communication channel." T-Comm 18, no. 11 (2024): 45–52. https://doi.org/10.36724/2072-8735-2024-18-11-45-52.

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This paper introduces a synthesis of an algorithm for estimating the parameters of a communication channel based on the observed M-QAM signal with a known information sequence. The proposed algorithm leverages the method of moments, expressed in the form of the Tikhonov A.N. functional, which allows for the efficient estimation of various channel parameters. These parameters include the amplitude, phase, and frequency shift of the received signal. The phase noise is also incorporated into the phase model to provide a more accurate reflection of real-world channel conditions. The estimation pro

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Axelsson, Patrik, Umut Orguner, Fredrik Gustafsson, and Mikael Norrlöf. "ML Estimation of Process Noise Variance in Dynamic Systems." IFAC Proceedings Volumes 44, no. 1 (2011): 5609–14. http://dx.doi.org/10.3182/20110828-6-it-1002.00543.

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