Zeitschriftenartikel: „Estimation of the process noise“

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Gillard, Nicolas, Étienne Belin und François Chapeau-Blondeau. „Stochastic Resonance with Unital Quantum Noise“. Fluctuation and Noise Letters 18, Nr. 03 (16.07.2019): 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, or the whole family of Pauli noises. As the level of noise is increased, we report the possibility of non-standard behaviors where the estimation efficiency does not necessarily deteriorate uniformly, but can experience non-monotonic variations. Regimes are found where higher noise levels prove more favorable to estimation. Such behaviors are related to stochastic resonance effects in signal estimation, shown here feasible for the first time with unital quantum noises. The results provide enhanced appreciation of quantum noise or decoherence, manifesting that it is not always detrimental for quantum information processing.

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Wang, Chen, Yao-Wu Shi, Lan-Xiang Zhu, Li-Fei Deng, Yi-Ran Shi und De-Min Wang. „Auto-regressive moving average parameter estimation for 1/f process under colored Gaussian noise background“. Journal of Algorithms & Computational Technology 13 (Januar 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-stable distribution rather than Gaussian distribution. We define and consistently estimate the samples normalized cross-correlations of linear SαS processes, and propose a samples normalized cross-correlations-based auto-regressive moving average parameter estimation method effective in noisy environments. Simulation results of auto-regressive moving average parameter estimation exhibit good performance.

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Shin, Vladimir, Rebbecca T. Y. Thien und Yoonsoo Kim. „Receding Horizon Least Squares Estimator with Application to Estimation of Process and Measurement Noise Covariances“. Mathematical Problems in Engineering 2018 (19.11.2018): 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 are obtained. They include derivation of a recursive form for the receding horizon covariance estimator and iteration procedure for selection of the best horizon length. Efficiency of the covariance estimator is demonstrated through its implementation and performance on dynamical systems with an arbitrary number of process and measurement noises.

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Moon, Todd K., und Jacob H. Gunther. „Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates“. Entropy 22, Nr. 5 (19.05.2020): 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 significantly better parameter estimates, with improvement increasing with the stack depth. The estimation algorithm is essentially a vector RLS adaptive filter, with time-varying covariance matrix. Different ways of estimating the unknown covariance are presented, as well as a method to estimate the variances of the AR and observation noise. The notation is extended to vector autoregressive (VAR) processes. Simulation results demonstrate performance improvements in coefficient error and in spectrum estimation.

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Tan, Hanlin, Huaxin Xiao, Shiming Lai, Yu Liu und Maojun Zhang. „Pixelwise Estimation of Signal-Dependent Image Noise Using Deep Residual Learning“. Computational Intelligence and Neuroscience 2019 (09.09.2019): 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 residual noise estimator” (DRNE) for pixelwise noise-level estimation. We carefully design the architecture of the DRNE, which consists of a stack of customized residual blocks without any pooling or interpolation operation. The proposed DRNE formulates the process of noise estimation as pixel-to-pixel prediction. The experimental results show that the DRNE can achieve better performance on nonhomogeneous noise estimation than state-of-the-art methods. In addition, the DRNE can bring denoising performance gains in removing signal-dependent Gaussian noise when working with recent deep learning denoising methods.

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Bianchi, Federico, Simone Formentin und Luigi Piroddi. „Process noise covariance estimation via stochastic approximation“. International Journal of Adaptive Control and Signal Processing 34, Nr. 1 (07.11.2019): 63–76. http://dx.doi.org/10.1002/acs.3068.

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7

Ali, Suhad A., C. Elaf A. Abbood und Shaymaa Abdu LKadhm. „Salt and Pepper Noise Removal Using Resizable Window and Gaussian Estimation Function“. International Journal of Electrical and Computer Engineering (IJECE) 6, Nr. 5 (01.10.2016): 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, then increasing the size of the pixel window depending on some criteria to be enough to estimate the results. To estimates the pixels of image, the Gaussian estimation function is used. The resulted image quality is measured by the statistical quantity measures that's Peak Signal-to-Noise Ratio (PSNR) and The Structural Similarity (SSIM) metrics. The results illustrate the quality of the enhanced image compared with the other traditional techniques. The slight gradual of SSIM metric described the performance of the proposed method with high increasing of noise levels.</p>

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Ali, Suhad A., C. Elaf A. Abbood und Shaymaa Abdu LKadhm. „Salt and Pepper Noise Removal Using Resizable Window and Gaussian Estimation Function“. International Journal of Electrical and Computer Engineering (IJECE) 6, Nr. 5 (01.10.2016): 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, then increasing the size of the pixel window depending on some criteria to be enough to estimate the results. To estimates the pixels of image, the Gaussian estimation function is used. The resulted image quality is measured by the statistical quantity measures that's Peak Signal-to-Noise Ratio (PSNR) and The Structural Similarity (SSIM) metrics. The results illustrate the quality of the enhanced image compared with the other traditional techniques. The slight gradual of SSIM metric described the performance of the proposed method with high increasing of noise levels.</p>

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Feng, Bo, Mengyin Fu, Hongbin Ma, Yuanqing Xia und Bo Wang. „Kalman Filter With Recursive Covariance Estimation—Sequentially Estimating Process Noise Covariance“. IEEE Transactions on Industrial Electronics 61, Nr. 11 (November 2014): 6253–63. http://dx.doi.org/10.1109/tie.2014.2301756.

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10

Jwo, Dah-Jing, und Chun-Fan Pai. „Incorporation of Neural Network State Estimator for GPS Attitude Determination“. Journal of Navigation 57, Nr. 1 (Januar 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 noise and measurement noise covariance matrices are available. In this article, a neural network state estimator, which replaces the Kalman filter, will be incorporated into the attitude determination mechanism for estimating the attitude angles from the noisy raw attitude solutions. Employing the neural network estimator improves robustness compared to the Kalman filtering method when uncertainty in noise statistical knowledge exists. Simulation is conducted and a comparative evaluation based on the neural network estimator and Kalman filter is provided.

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Vorobeychikov, Sergey E., und 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, Nr. 4 (13.04.2020): 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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Gao, Wei, Jingchun Li, Guangtao Zhou und Qian Li. „Adaptive Kalman Filtering with Recursive Noise Estimator for Integrated SINS/DVL Systems“. Journal of Navigation 68, Nr. 1 (15.08.2014): 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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Chuang, Chia-Hua, und Chun-Liang Lin. „On Robust State Estimation of Gene Networks“. Biomedical Engineering and Computational Biology 2 (Januar 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 demonstration.

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YE, ZHIPIN, und CHUANGYIN DANG. „PARAMETER ESTIMATION FOR LINEAR FRACTIONAL STABLE NOISE PROCESS“. Journal of Circuits, Systems and Computers 14, Nr. 02 (April 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 minimizing square errors, several methods for estimating these two parameters are presented. Detailed tables and graphs have been included in extensive simulations which show the methods are good estimates.

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Balakina, N., und A. Balakin. „Automation of the Process of Measuring and Evaluating Intermittent Industrial Noise“. Bulletin of Science and Practice 7, Nr. 4 (15.04.2021): 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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Musunuri, Yogendra Rao, und Oh-Seol Kwon. „State Estimation Using a Randomized Unscented Kalman Filter for 3D Skeleton Posture“. Electronics 10, Nr. 8 (19.04.2021): 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 to that of conventional methods based on experimental results.

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Liu, Shing-Hong, Cheng-Hsiung Hsieh, Wenxi Chen und Tan-Hsu Tan. „ECG Noise Cancellation Based on Grey Spectral Noise Estimation“. Sensors 19, Nr. 4 (15.02.2019): 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 stage of the proposed GSNC scheme, the input ECG signal is decomposed by the EMD to obtain a set of intrinsic mode functions (IMFs). Then, the noise energies of IMFs are estimated by the GSNE. When an IMF is considered as noisy one, it is forwarded to the second stage for further check. In the second stage, the suspicious IMFs are reconstructed and decomposed by the EEMD. Then the IMFs are discriminated with a threshold. If the IMF is considered as noisy, it is discarded in the reconstruction process of the ECG signal. The proposed GSNC scheme is justified by forty-three ECG signal datasets from the MIT-BIH cardiac arrhythmia database where the PLn and EMG noise are under consideration. The results indicate that the proposed GSNC scheme outperforms the traditional EMD and EEMD based noise cancellation schemes in the given datasets.

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Niu, S., und D. G. Fisher. „Simultaneous estimation of process parameters, noise variance, and signal-to-noise ratio“. IEEE Transactions on Signal Processing 43, Nr. 7 (Juli 1995): 1725–28. http://dx.doi.org/10.1109/78.398737.

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Dong, Lingyan, Hongli Xu, Xisheng Feng, Xiaojun Han und Chuang Yu. „An Adaptive Target Tracking Algorithm Based on EKF for AUV with Unknown Non-Gaussian Process Noise“. Applied Sciences 10, Nr. 10 (15.05.2020): 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 algorithm is applied to estimate the process noise statistics. We use a Gaussian mixture distribution to model the non-Gaussian noisy forward speed of AUV and MRS. We use a von-Mises distribution to model the noisy heading of AUV and MRS. The variational Bayesian algorithm is applied to estimate the parameters of these distributions, and then the PNCM can be calculated. The prediction error of TT-EKF is online compensated by using a multilayer neural network, and the neural network is online trained during the target tracking process. Matlab simulation and experimental data analysis results verify the effectiveness of the proposed method.

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Liu, Shi Lin, und Zheng Pei. „Voice Activity Based on Noise Estimation in Noisy Environments“. Applied Mechanics and Materials 239-240 (Dezember 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 obtain the same accurate data by computing less than the one with decision trees.

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Jalil, Bushra, Zunera Jalil, Eric Fauvet und Olivier Laligant. „Edge-Preserving Image Denoising Based on Lipschitz Estimation“. Applied Sciences 11, Nr. 11 (31.05.2021): 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 are important, more specifically, for visual perception. The most significant information about the structure of the image or type of the signal is often hidden inside these transitions. Therefore it is necessary to preserve them. This paper introduces a method to reduce noise and to preserve edges while performing Non-Destructive Testing (NDT). The method computes Lipschitz exponents of transitions to identify the level of discontinuity. Continuous wavelet transform-based multi-scale analysis highlights the modulus maxima of the respective transitions. Lipschitz values estimated from these maxima are used as a measure to preserve edges in the presence of noise. Experimental results show that the noisy data sample and smoothness-based heuristic approach in the spatial domain restored noise-free images while preserving edges.

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Safarinejadian, Behrouz, Nasrin Kianpour und Mojtaba Asad. „State estimation in fractional-order systems with coloured measurement noise“. Transactions of the Institute of Measurement and Control 40, Nr. 6 (15.03.2017): 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 measurement noise, are proposed. The accuracy of the equations and relations is confirmed in several theorems. The validity and effectiveness of the proposed algorithms are verified by simulation results and compared with previous work. Results show that for linear and nonlinear fractional-order systems with coloured noise, the proposed methods are more accurate than conventional methods regarding estimation error and estimation error covariance. Simulation results demonstrate that the proposed algorithms can accurately perform estimation in fractional-order systems with coloured measurement noise.

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PATEL, HIREN G., und SHAMBHU N. SHARMA. „SOME EVOLUTION EQUATIONS FOR AN ORNSTEIN–UHLENBECK PROCESS-DRIVEN DYNAMICAL SYSTEM“. Fluctuation and Noise Letters 11, Nr. 04 (Dezember 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 to develop the theory of the OU process-driven Duffing–van der Pol (DvdP) system of this paper. Notably, the noise analysis of the Duffing–van der Pol system, especially from the estimation-theoretic viewpoint, under the colored noise influence is not available yet in literature. Numerical experimentations with three different sets of data are demonstrated to examine the efficacy of analytical findings of this paper. The results of this paper will be of interest to noise scientists, especially research communities in systems and control, looking for the estimation-theoretic scenarios of the colored noise-driven "vector" stochastic differential system.

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Caballero-Águila, R., I. García-Garrido und 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 are (i) the process noise and all the sensor noises are one-step autocorrelated; (ii) different sensor noises are one-step cross-correlated; and (iii) the process noise and each sensor noise are two-step cross-correlated. Under these assumptions and by an innovation approach, recursive algorithms for the optimal linear filter are derived by using the two basic estimation fusion structures; more specifically, both centralized and distributed fusion estimation algorithms are proposed. The accuracy of these estimators is measured by their error covariance matrices, which allow us to compare their performance in a numerical simulation example that illustrates the feasibility of the proposed filtering algorithms and shows a comparison with other existing filters.

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Wang, Chen, Yao-Wu Shi, Lan-Xiang Zhu, Li-Fei Deng, Yi-Ran Shi und De-Min Wang. „α-spectrum estimation for 1/f processes in noisy environments“. Noise & Vibration Worldwide 50, Nr. 2 (Februar 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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Axelsson, Patrik, Umut Orguner, Fredrik Gustafsson und Mikael Norrlöf. „ML Estimation of Process Noise Variance in Dynamic Systems“. IFAC Proceedings Volumes 44, Nr. 1 (Januar 2011): 5609–14. http://dx.doi.org/10.3182/20110828-6-it-1002.00543.

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Wu, Nan, Lei Chen, Yongjun Lei und 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, Nr. 14 (24.02.2016): 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 in the absence of prior information, the unknown input is estimated effectively in terms of magnitude, a positive definite matrix of process noise covariance which is close to the optimal value is constructed real-timely, and the state estimation error approximates the error lower bound of the optimal estimation. The estimation accuracy of the proposed algorithm is similar to that of the current statistical model algorithm using accurate prior information.

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Kumar, R. Suresh, und P. Manimegalai. „Detection and Separation of Eeg Artifacts Using Wavelet Transform“. International Journal of Informatics and Communication Technology (IJ-ICT) 7, Nr. 3 (01.12.2018): 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-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.

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Batra, Deepak, Sanjay Sharma und Amit Kumar Kohli. „Improved Parameter Estimation for First-Order Markov Process“. Research Letters in Signal Processing 2009 (2009): 1–2. http://dx.doi.org/10.1155/2009/186250.

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This correspondence presents a linear transformation, which is used to estimate correlation coefficient of first-order Markov process. It outperforms zero-forcing (ZF), minimum mean-squared error (MMSE), and whitened least-squares (WTLSs) estimators by controlling output noise variance at the cost of increased computational complexity.

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GRECKSCH, WILFRIED, und CONSTANTIN TUDOR. „A FILTERING PROBLEM FOR A LINEAR STOCHASTIC EVOLUTION EQUATION DRIVEN BY A FRACTIONAL BROWNIAN MOTION“. Stochastics and Dynamics 08, Nr. 03 (September 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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Twigg, P. M., und M. Thomson. „On-Line Noise Estimation for Adaptive SPC Loop Control“. Transactions of the Institute of Measurement and Control 17, Nr. 3 (August 1995): 112–19. http://dx.doi.org/10.1177/014233129501700303.

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This paper describes a method of measuring the variance of a noisy process on-line, enabling the application of adaptive Statistical Process Control (SPC) to supervise a continuous process loop. A fundamental requirement of SPC techniques is that the data are independent. Process control loops typically generate autocorrelated data, and this tends to create an increase in the false alarm rate when attempting to apply SPC techniques. The method presented here produces an uncorrected sequence from potentially correlated data, overcoming this problem. The SPC supervisory unit acts externally to the control loop preventing any lags in feedback. When these SPC charts are used in a supervisory role with a conventional PID controller, application of individual PID terms achieves a much quieter control with little reduction in performance and without the need for prior off-line noise tests. The results of tests in simulation and on a steam-water heat exchanger are presented.

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Hero, A. O. „Timing estimation for a filtered Poisson process in Gaussian noise“. IEEE Transactions on Information Theory 37, Nr. 1 (1991): 92–106. http://dx.doi.org/10.1109/18.61107.

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Lee, Jong Hyup. „Estimation for the autoregressive moving average process observed with noise“. Journal of Applied Statistics 23, Nr. 6 (Dezember 1996): 589–600. http://dx.doi.org/10.1080/02664769623955.

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Zilinskas, Antanas. „Small Sample Estimation of Parameters for Wiener Process with Noise“. Communications in Statistics - Theory and Methods 40, Nr. 16 (15.08.2011): 3020–28. http://dx.doi.org/10.1080/03610926.2011.562788.

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35

Pardede, Hilman, Kalamullah Ramli, Yohan Suryanto, Nur Hayati und Alfan Presekal. „Speech Enhancement for Secure Communication Using Coupled Spectral Subtraction and Wiener Filter“. Electronics 8, Nr. 8 (14.08.2019): 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 methods work by assuming noise is present all the time, so the voice activity detector (VAD) is applied to detect the non-speech period to update the noise estimate. Unfortunately, this assumption is not valid for the decrypted speech. Since the encryption process is applied only when speech is detected, distortions from the secure communication system are characteristically different. They exist when speech is present. Therefore, a noise estimator that is able to update noise even when speech is present is needed. However, most noise estimator techniques only adapt to slow changes of noise to avoid over-estimation of noise, making them unsuitable for this task. In this paper, we propose a speech enhancement technique to improve the quality of speech from secure communication. We use a combination of the Wiener filter and spectral subtraction for the noise estimator, so our method is better at tracking fast changes of noise without over-estimating them. Our experimental results on various communication channels indicate that our method is better than other popular noise estimators and speech enhancement methods.

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Marchuk, Vladimir, Igor Shrafel, Dmitry Chernyshov, Alexander Minaev und Stepan Buryakov. „Partition optimization for a random process realization to estimate its expected value“. Serbian Journal of Electrical Engineering 14, Nr. 3 (2017): 333–42. http://dx.doi.org/10.2298/sjee1703333m.

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The paper provides an analytical proof the optimal number of partitions of a non-stationary random process realization, which is necessary for estimating its expected value when using ?the estimation reproduction? method for signal processing. This method allows to process signal with a limited volume of priori information about the desired signal function and statistical characteristics of the additive noise component.

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Fraanje, Rufus, René Beltman, Fidelis Theinert, Michiel van Osch, Teade Punter und John Bolte. „Sensor Fusion of Odometer, Compass and Beacon Distance for Mobile Robots“. International Journal of Artificial Intelligence and Machine Learning 10, Nr. 1 (Januar 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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Liu, Fang, Keyu Li, Wei Ge Liang und Fu Qing Tian. „EKF with Measurement Noise Estimation Based on Wavelet Transform and Application for Target Tracking“. Applied Mechanics and Materials 519-520 (Februar 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 variance estimator is used to estimate the measurement noise, which can improve the precision in EKF of target tracking by combining with EKF. Finally, the method of Monte Carlo simulation is used to prove its effectiveness and practicality.

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Song, Yuan Yun, Wan Chun Chen und Xing Liang Yin. „An Estimation Approach Based on Predictive Filtering to Missile Guidance“. Advanced Materials Research 383-390 (November 2011): 38–44. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.38.

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An approach for estimation to states of missile guidance based on predictive filtering is derived for the measurement process with high and low frequency noise. Analyze the influence of various noises on miss distance. Comparing with the extended Kalman filter, the simulation results indicate that this new method is able to improve accuracy of the guidance system with less estimation error.

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Jiang, Haonan, und Yuanli Cai. „Adaptive Fifth-Degree Cubature Information Filter for Multi-Sensor Bearings-Only Tracking“. Sensors 18, Nr. 10 (26.09.2018): 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 filtering framework. With a sensor selection strategy developed using observability theory and a recursive process noise covariance estimation procedure derived using the covariance matching principle, the proposed filtering algorithm demonstrates better estimation accuracy and filtering stability. Simulation results validate the superiority of the AFCIF.

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Wang, Dapeng, Hai Zhang und Baoshuang Ge. „Adaptive Unscented Kalman Filter for Target Tacking with Time-Varying Noise Covariance Based on Multi-Sensor Information Fusion“. Sensors 21, Nr. 17 (29.08.2021): 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 isolated from the state estimation; the second layer is the fusion structure to calculate the optimal matrix weights and gives the final optimal state estimations. Based on the hypothesis testing theory with the Mahalanobis distance, the new adaptive UKF scheme utilizes both the innovation and the residual sequences to adapt the process noise covariance timely. The results of the target tracking simulations indicate that the proposed method is effective under the condition of time-varying process-error and measurement noise covariance.

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Russo, Paolo, Fabiana Di Ciaccio und Salvatore Troisi. „DANAE++: A Smart Approach for Denoising Underwater Attitude Estimation“. Sensors 21, Nr. 4 (22.02.2021): 1526. http://dx.doi.org/10.3390/s21041526.

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One of the main issues for the navigation of underwater robots consists in accurate vehicle positioning, which heavily depends on the orientation estimation phase. The systems employed to this end are affected by different noise typologies, mainly related to the sensors and to the irregular noise of the underwater environment. Filtering algorithms can reduce their effect if opportunely configured, but this process usually requires fine techniques and time. This paper presents DANAE++, an improved denoising autoencoder based on DANAE (deep Denoising AutoeNcoder for Attitude Estimation), which is able to recover Kalman Filter (KF) IMU/AHRS orientation estimations from any kind of noise, independently of its nature. This deep learning-based architecture already proved to be robust and reliable, but in its enhanced implementation significant improvements are obtained in terms of both results and performance. In fact, DANAE++ is able to denoise the three angles describing the attitude at the same time, and that is verified also using the estimations provided by an extended KF. Further tests could make this method suitable for real-time applications in navigation tasks.

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de Brauwere, Anouk, Rik Pintelon, Fjo De Ridder, Johan Schoukens und Willy Baeyens. „Estimation of heteroscedastic measurement noise variances“. Chemometrics and Intelligent Laboratory Systems 86, Nr. 1 (März 2007): 130–38. http://dx.doi.org/10.1016/j.chemolab.2006.09.001.

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44

Wang, Xinmei, Leimin Wang, Longsheng Wei und Feng Liu. „Estimation of Object Motion State Based on Adaptive Decorrelation Kalman Filtering“. Journal of Advanced Computational Intelligence and Intelligent Informatics 23, Nr. 4 (20.07.2019): 749–57. http://dx.doi.org/10.20965/jaciii.2019.p0749.

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To estimate the motion state of object feature point in image space, an adaptive decorrelation Kalman filtering model is proposed in this paper. The model is based on the Kalman filtering method. A first-order Markov sequence model is used to describe the colored measurement noise. To eliminate the colored noise, the measurement equation is reconstructed and then a cross-correlation between the process noise and the newly measurement noise is established. To eliminate the noise cross-correlation, a reconstructed process equation is proposed. According to the new process and measurement equations, and the noise mathematical characteristics of the standard Kalman filtering method, the parameters involved in the new process equation can be acquired. Then the noise cross-correlation can be successfully eliminated, and a decorrelation Kalman filtering model can be obtained. At the same time, for obtaining a more accurate measurement noise variance, an adaptive recursive algorithm is proposed to update the measurement noise variance based on the correlation method. It overcomes the limitations of traditional correlation methods used for noise variance estimation, thus, a relatively accurate Kalman filtering model can be obtained. The simulation shows that the proposed method improves the estimation accuracy of the motion state of object feature point.

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Turajlic, Emir, Alen Begović und Namir Škaljo. „Application of Artificial Neural Network for Image Noise Level Estimation in the SVD domain“. Electronics 8, Nr. 2 (01.02.2019): 163. http://dx.doi.org/10.3390/electronics8020163.

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The blind additive white Gaussian noise level estimation is an important and a challenging area of digital image processing with numerous applications including image denoising and image segmentation. In this paper, a novel block-based noise level estimation algorithm is proposed. The algorithm relies on the artificial neural network to perform a complex image patch analysis in the singular value decomposition (SVD) domain and to evaluate noise level estimates. The algorithm exhibits the capacity to adjust the effective singular value tail length with respect to the observed noise levels. The results of comparative analysis show that the proposed ANN-based algorithm outperforms the alternative single stage block-based noise level estimating algorithm in the SVD domain in terms of mean square error (MSE) and average error for all considered choices of block size. The most significant improvements in MSE levels are obtained at low noise levels. For some test images, such as “Car” and “Girlface”, at σ = 1 , these improvements can be as high as 99% and 98.5%, respectively. In addition, the proposed algorithm eliminates the error-prone manual parameter fine-tuning and automates the entire noise level estimation process.

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Murugan, S. Sakthivel, V. Natarajan und R. Rajesh Kumar. „Noise Model Analysis and Estimation of Effect due to Wind Driven Ambient Noise in Shallow Water“. International Journal of Oceanography 2011 (26.12.2011): 1–4. http://dx.doi.org/10.1155/2011/950838.

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Signal transmission in ocean using water as a channel is a challenging process due to attenuation, spreading, reverberation, absorption, and so forth, apart from the contribution of acoustic signals due to ambient noises. Ambient noises in sea are of two types: manmade (shipping, aircraft over the sea, motor on boat, etc.) and natural (rain, wind, seismic, etc.), apart from marine mammals and phytoplanktons. Since wind exists in all places and at all time: its effect plays a major role. Hence, in this paper, we concentrate on estimating the effects of wind. Seven sets of data with various wind speeds ranging from 2.11 m/s to 6.57 m/s were used. The analysis is performed for frequencies ranging from 100 Hz to 8 kHz. It is found that a linear relationship between noise spectrum and wind speed exists for the entire frequency range. Further, we developed a noise model for analyzing the noise level. The results of the empirical data are found to fit with results obtained with the aid of noise model.

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Wang, H., Y. Zhang, H. Bao und T. Qiu. „Non-Linear Transform-Based Robust Adaptive Latency Change Estimation of Evoked Potentials“. Methods of Information in Medicine 41, Nr. 04 (2002): 331–36. http://dx.doi.org/10.1055/s-0038-1634390.

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Summary Objectives: To improve the latency change estimation of evoked potentials (EP) under the lower order -stable noise conditions by proposing and analyzing a new adaptive EP latency change detection algorithm (referred to as the NLST). Methods: The NLST algorithm is based on the fractional lower order moment and the nonlinear transform for the error function. The computer simulation and data analysis verify the robustness of the new algorithm. Results: The theoretical analysis shows that the iteration equation of the NLST transforms the lower order α-stable process en (k) into a second order moment process by a nonlinear transform. The simulations and the data analysis showed the robustness of the NLST under the lower order α-stable noise conditions. Conclusions: The new algorithm is robust under the lower order -stable noise conditions, and it also provides a better performance than the DLMS, DLMP and SDA algorithms without the need to estimate thevalue of the EP signals and noises.

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Ding, Weidong, Jinling Wang, Chris Rizos und Doug Kinlyside. „Improving Adaptive Kalman Estimation in GPS/INS Integration“. Journal of Navigation 60, Nr. 3 (09.08.2007): 517–29. http://dx.doi.org/10.1017/s0373463307004316.

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The central task of GPS/INS integration is to effectively blend GPS and INS data together to generate an optimal solution. The present data fusion algorithms, which are mostly based on Kalman filtering (KF), have several limitations. One of those limitations is the stringent requirement on precise a priori knowledge of the system models and noise properties. Uncertainty in the covariance parameters of the process noise (Q) and the observation errors (R) may significantly degrade the filtering performance. The conventional way of determining Q and R relies on intensive analysis of empirical data. However, the noise levels may change in different applications. Over the past few decades adaptive KF algorithms have been intensively investigated with a view to reducing the influence of the Q and R definition errors. The covariance matching method has been shown to be one of the most promising techniques. This paper first investigates the utilization of an online stochastic modelling algorithm with regards to its parameter estimation stability, convergence, optimal window size, and the interaction between Q and R estimations. Then a new adaptive process noise scaling algorithm is proposed. Without artificial or empirical parameters being used, the proposed adaptive mechanism has demonstrated the capability of autonomously tuning the process noise covariance to the optimal magnitude, and hence improving the overall filtering performance.

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Wayson, Roger L., Kenneth Kaliski, John M. MacDonald, Erik M. Salomons und Darlene D. Reiter. „Data Collection and Modeling Results to Permit Estimation of Meteorological Effects on Roadway Noise“. Transportation Research Record: Journal of the Transportation Research Board 2673, Nr. 11 (12.06.2019): 243–53. http://dx.doi.org/10.1177/0361198119849573.

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The estimation of absolute road traffic noise levels without including the effects of meteorology is thought to be a major source of error in the estimation process commonly used in the United States. In response, the Transportation Research Board-sponsored NCHRP 25-52, Meteorological Effects on Roadway Noise, to collect highway noise data under different meteorological conditions, document the meteorological effects on roadway noise propagation under different atmospheric conditions, develop best practices, and provide guidance on how to (a) quantify meteorological effects on roadway noise propagation and (b) explain those effects to the public. The completed project involved collecting and analyzing 35,000 min of sound and meteorological data at 16 barrier and no-barrier measurement positions adjacent to Interstate 17 in Phoenix, Arizona. This report provides information on the data collection and the modeling recommendations. The database assembled is thought to be among the best available in the United States to permit analysis of meteorological effects on roadway noise. The study recommendations will advance the methodology for estimating the meteorological effects on roadway noise in the United States.

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Zhang, Zhiyu, Jinzhe Qiu und Wentao Ma. „Adaptive Extended Kalman Filter with Correntropy Loss for Robust Power System State Estimation“. Entropy 21, Nr. 3 (18.03.2019): 293. http://dx.doi.org/10.3390/e21030293.

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Monitoring the current operation status of the power system plays an essential role in the enhancement of the power grid for future requirements. Therefore, the real-time state estimation (SE) of the power system has been of widely-held concern. The Kalman filter is an outstanding method for the SE, and the noise in the system is generally assumed to be Gaussian noise. In the actual power system however, these measurements are usually disturbed by non-Gaussian noises in practice. Furthermore, it is hard to get the statistics of the state noise and measurement noise. As a result, a novel adaptive extended Kalman filter with correntropy loss is proposed and applied for power system SE in this paper. Firstly, correntropy is used to improve the robustness of the EKF algorithm in the presence of non-Gaussian noises and outliers. In addition, an adaptive update mechanism of the covariance matrixes of the measurement and process noises is introduced into the EKF with correntropy loss to enhance the accuracy of the algorithm. Extensive simulations are carried out on IEEE 14-bus and IEEE 30-bus test systems to verify the feasibility and robustness of the proposed algorithm.

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