Relevant bibliographies by topics / Estimation of the process noise

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Estimation of the process noise'

Author: Grafiati
Published: 28 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Estimation of the process noise"

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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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Dissertations / Theses on the topic "Estimation of the process noise"

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Anilkumar, A. K. "Application Of Controlled Random Search Optimization Technique In MMLE With Process Noise." Thesis, Indian Institute of Science, 2000. https://etd.iisc.ac.in/handle/2005/232.

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Generally in most of the applications of estimation theory using the Method of Maximum Likelihood Estimation (MMLE) to dynamical systems one deals with a situation where only the measurement noise alone is present. However in many present day applications where modeling errors and random state noise input conditions occur it has become necessary for MMLE to handle measurement noise as well as process noise. The numerical algorithms accounting for both measurement and process noise require significantly an order of magnitude higher computer time and memory. Further more, implementation difficul
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Anilkumar, A. K. "Application Of Controlled Random Search Optimization Technique In MMLE With Process Noise." Thesis, Indian Institute of Science, 2000. http://hdl.handle.net/2005/232.

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Generally in most of the applications of estimation theory using the Method of Maximum Likelihood Estimation (MMLE) to dynamical systems one deals with a situation where only the measurement noise alone is present. However in many present day applications where modeling errors and random state noise input conditions occur it has become necessary for MMLE to handle measurement noise as well as process noise. The numerical algorithms accounting for both measurement and process noise require significantly an order of magnitude higher computer time and memory. Further more, implementation difficul
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Barajas, Leandro G. "Process Control in High-Noise Environments Using A Limited Number Of Measurements." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/7741.

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The topic of this dissertation is the derivation, development, and evaluation of novel hybrid algorithms for process control that use a limited number of measurements and that are suitable to operate in the presence of large amounts of process noise. As an initial step, affine and neural network statistical process models are developed in order to simulate the steady-state system behavior. Such models are vitally important in the evaluation, testing, and improvement of all other process controllers referred to in this work. Afterwards, fuzzy logic controller rules are assimilated into a mathe
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Constant, Camille. "Modélisation stochastique et analyse statistique de la pulsatilité en neuroendocrinologie." Thesis, Poitiers, 2019. http://www.theses.fr/2019POIT2330.

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L’objectif de cette thèse est de proposer plusieurs modèles probabilistes pour représenter l’activité calcique des neurones et comprendre son implication dans la sécrétion d’hormone GnRH. Ce travail s’appuie sur des expériences réalisées à l’INRA Centre Val-de-Loire. Le Chapitre 1 propose une modélisation continue, où nous étudions un processus markovien de type shot-noise. Le Chapitre 2 étudie un modèle discret de type AR(1) basé sur la discrétisation du modèle du Chapitre 1 et propose une première estimation des paramètres. Le Chapitre 3 propose un autre modèle discret de type AR(1) où les i
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Sun, Yucheng. "Essays in volatility estimation based on high frequency data." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/402831.

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Based on high-frequency price data, this thesis focuses on estimating the realized covariance and the integrated volatility of asset prices, and applying volatility estimation to price jump detection. The first chapter uses the LASSO procedure to regularize some estimators of high dimensional realized covariance matrices. We establish theoretical properties of the regularized estimators that show its estimation precision and the probability that they correctly reveal the network structure of the assets. The second chapter proposes a novel estimator of the integrated volatility which is the qua
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Maillet, Raphaël. "Analyse statistique et probabiliste de systèmes diffusifs en présence de bruit." Electronic Thesis or Diss., Université Paris sciences et lettres, 2024. http://www.theses.fr/2024UPSLD025.

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Cette thèse traite du comportement en temps long des équations stochastiques de Fokker-Planck en présence d’un bruit commun additif et présente des méthodes statistiques pour estimer la mesure invariante des processus de diffusion ergodiques multidimensionnels à partir de données bruitées. Dans la première partie, nous analysons les équations différentielles partielles stochastiques de type Fokker-Planck non linéaires, obtenues comme la limite du champ moyen de systèmes de particules en interaction dirigés par des bruits browniens idiosyncrasiques et en présence de bruit commun. Nous établisso
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Jaoua, Nouha. "Estimation Bayésienne non Paramétrique de Systèmes Dynamiques en Présence de Bruits Alpha-Stables." Phd thesis, Ecole Centrale de Lille, 2013. http://tel.archives-ouvertes.fr/tel-00929691.

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Dans un nombre croissant d'applications, les perturbations rencontrées s'éloignent fortement des modèles classiques qui les modélisent par une gaussienne ou un mélange de gaussiennes. C'est en particulier le cas des bruits impulsifs que nous rencontrons dans plusieurs domaines, notamment celui des télécommunications. Dans ce cas, une modélisation mieux adaptée peut reposer sur les distributions alpha-stables. C'est dans ce cadre que s'inscrit le travail de cette thèse dont l'objectif est de concevoir de nouvelles méthodes robustes pour l'estimation conjointe état-bruit dans des environnements
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Iskander, D. R. "The Generalised Bessel function K distribution and its application to the detection of signals in the presence of non-Gaussian interference." Thesis, Queensland University of Technology, 1997.

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Chen, Hao. "Noise enhanced signal detection and estimation." Related electronic resource:, 2007. http://proquest.umi.com/pqdweb?did=1342743841&sid=2&Fmt=2&clientId=3739&RQT=309&VName=PQD.

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Bareš, Robert. "Environmentally adaptive noise estimation for active sonar." Thesis, Cardiff University, 2012. http://orca.cf.ac.uk/18588/.

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Noise is frequently encountered when processing data from the natural environment, and is of particular concern for remote-sensing applications where the accuracy of data gathered is limited by the noise present. Rather than merely accepting that sonar noise results in unavoidable error in active sonar systems, this research explores various methodologies to reduce the detrimental effect of noise. Our approach is to analyse the statistics of sonar noise in trial data, collected by a long-range active sonar system in a shallow water environment, and apply this knowledge to target detection. Our
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Books on the topic "Estimation of the process noise"

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Kesavan, R. Process planning and cost estimation. 2nd ed. New Age International Ltd., 2009.

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Johnson, A. Process dynamics, estimation and control. Peregrinus on behalf of the Institution of Electrical Engineers, 1985.

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Adithan, M. Process planning and cost estimation. New Age International (P) Ltd., Publishers, 2007.

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Engineers, Institution of Electrical, ed. Process dynamics estimation and control. P. Peregrinus Ltd. on behalf of the Institution of Electrical Engineers, 1985.

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Gershon, Eli. Advanced Topics in Control and Estimation of State-Multiplicative Noisy Systems. Springer London, 2013.

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Standardization, International Organization for, ed. Acoustics: Determination of occupational noise exposure and estimation of noise induced hearing impairment. 2nd ed. International Organization for Standardization, 1990.

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Aït-Sahalia, Yacine. Ultra high frequency volatility estimation with dependent microstructure noise. National Bureau of Economic Research, 2005.

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Aït-Sahalia, Yacine. Ultra high frequency volatility estimation with dependent microstructure noise. National Bureau of Economic Research, 2005.

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Ferguson, Ian. Dust and noise in the construction process. HSE Books, 1995.

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Ruhmer, W. T. Handbook on the estimation of metallurgical process costs. 2nd ed. Mintek, 1996.

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Book chapters on the topic "Estimation of the process noise"

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Ma, Hongbin, Liping Yan, Yuanqing Xia, and Mengyin Fu. "Kalman Filter with Recursive Process Noise Covariance Estimation." In Kalman Filtering and Information Fusion. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0806-6_3.

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Bulut, Yalcin, D. Vines-Cavanaugh, and Dionisio Bernal. "Process and Measurement Noise Estimation for Kalman Filtering." In Structural Dynamics, Volume 3. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9834-7_36.

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Tatsis, Konstantinos E., Vasilis K. Dertimanis, and Eleni N. Chatzi. "Adaptive Process and Measurement Noise Identification for Recursive Bayesian Estimation." In Model Validation and Uncertainty Quantification, Volume 3. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47638-0_39.

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Fieldhouse, Harry, and David Cole. "State Estimation and Sensorimotor Noise in a Driver Steering Model with a Gaussian Process Internal Model." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_10.

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AbstractRefinements to a mathematical model of human drivers’ steering control incorporating driver learning are reported. State estimation and realistic sensorimotor noise sources are introduced to the driver model to better represent neural processes. It is found that the driver model exhibits the expected learning behaviour in terms of estimation and control performance. Further work is planned to validate the model experimentally.
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Wang, Xiaoliang, Shufan Wu, Zeyu Kang, Deren Gong, and Jianhui Yu. "Adaptive Attitude Estimation and Control of Hypersonic Re-Entry Vehicle with Unknown Process Noise." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8155-7_75.

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Fujii, Tomohiro, and Masao Hirokawa. "A Data Concealing Technique with Random Noise Disturbance and a Restoring Technique for the Concealed Data by Stochastic Process Estimation." In International Symposium on Mathematics, Quantum Theory, and Cryptography. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5191-8_11.

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Abstract We propose a technique to conceal data on a physical layer by disturbing them with some random noises, and moreover, a technique to restore the concealed data to the original ones by using the stochastic process estimation. Our concealing-restoring system manages the data on the physical layer from the data link layer. In addition to these proposals, we show the simulation result and some applications of our concealing-restoring technique.
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Muñoz-Gracia, M. P., J. Pages-Fita, and M. Marti-Recober. "Estimation of ARMA Process Parameters and Noise Variance by Means of a Non Linear Filtering Algorithm." In Compstat. Physica-Verlag HD, 1988. http://dx.doi.org/10.1007/978-3-642-46900-8_49.

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Hajiyev, Chingiz, and Demet Cilden-Guler. "Process and Measurement Noise Covariance Tuning in Kalman-Based Estimator Aided by SVD." In New Technologies and Developments in Unmanned Systems. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-37160-8_45.

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Hendriks, Richard C., Timo Gerkmann, and Jesper Jensen. "Noise PSD Estimation." In DFT-Domain Based Single-Microphone Noise Reduction for Speech Enhancement. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-031-02564-8_6.

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Westphal, L. C. "State estimation in noise." In Sourcebook of Control Systems Engineering. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-1805-1_28.

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Conference papers on the topic "Estimation of the process noise"

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Hajiyev, C., and U. Hacizade. "Q-adaptive AKF for Estimation of UAV dynamics in the Case of Biased Process Noise." In 2024 XXXIII International Scientific Conference Electronics (ET). IEEE, 2024. http://dx.doi.org/10.1109/et63133.2024.10721515.

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Eden, D. A., and A. N. Rothwell. "Electrochemical Noise Data: Analysis, Interpretation and Presentation." In CORROSION 1992. NACE International, 1992. https://doi.org/10.5006/c1992-92292.

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Abstract Electrochemical techniques for the assessment of corrosion processes have been widely adopted for many years. Traditional techniques typically use an applied voltage signal and the current response is monitored. This enables an estimation of the corrosion rate to be made. Recent studies have used the technique of Electrochemical Noise, which involves monitoring the spontaneous fluctuations in potential and current, in the absence of an applied signal. This approach has certain advantages, particularly in that the spontaneous evolution of the corrosion process(es) may be studied. Some
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Wang, Yiqing, Mingming Song, Ye Xia, Wenjun Cao, and Limin Sun. "Joint Input-State Estimation Based on Recurrent Neural Network Assisted-Augmented Kalman Filter." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.0338.

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<p>The joint estimation of system states and unknown input loads in dynamic civil structures, based on limited observations, has garnered significant attention in recent years. A widely used method for this is the augmented Kalman filter (AKF), which extends the state vector to include unknown inputs, allowing for simultaneous state and input estimation. However, like the classical Kalman filter (KF), the AKF is highly sensitive to the tuning of hyperparameters—specifically, the covariance matrices of process and measurement noise—and to inaccuracies in the state-space model, which hampe
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Knapp, Marit, Christina Ivler, Marcos Berrios, Tom Berger, and Mark Tischler. "Kalman Filter Estimation of Rotor-State Flapping: An Optimization-based Approach with UH-60 Flight Test Data." In Vertical Flight Society 73rd Annual Forum & Technology Display. The Vertical Flight Society, 2017. http://dx.doi.org/10.4050/f-0073-2017-12185.

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Flight testing of explicit rotor-state feedback (RSF) fly-by-wire control laws showed that measuring rotor tip-path-plane (TPP) flapping, via a laser measurement system, provided additional lead to the control system. This resulted in superior handling qualities in turbulence and heavy winds and improved stability margins. However, a significant impediment to the adoption of explicitly measured RSF has been the difficulty in extracting reliable rotor measurements. Therefore, this paper describes the development of a Kalman filter that was designed to estimate rotor TPP coordinates, and remove
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Nguyen, H. N., and F. Guillemin. "On process noise covariance estimation." In 2017 25th Mediterranean Conference on Control and Automation (MED). IEEE, 2017. http://dx.doi.org/10.1109/med.2017.7984305.

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Lee, Cheol W. "Multirate Estimation for the Machining Process Under Multirate Noise." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-42187.

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Asynchronous measurement of process output characterizes a typical machining process in batch production. Various sensors are used for in-process measurement of process variables such as powers, forces, and vibration while the quality of parts and the tool conditions can be measured only by the postprocess inspection in most applications. The multirate scheme is being proposed as an efficient tool for integrating real-time sensor signals with postprocess inspection data for estimating immeasurable variables of the machining process. The machining process is subject to the process noise of vary
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Ilhe, Paul, Francois Roueff, Eric Moulines, and Antoine Souloumiac. "Nonparametric estimation of a shot-noise process." In 2016 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2016. http://dx.doi.org/10.1109/ssp.2016.7551709.

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Zhao, Ye, Xiao He, and Donghua Zhou. "Fault estimation with biased process noise covariances." In 2017 36th Chinese Control Conference (CCC). IEEE, 2017. http://dx.doi.org/10.23919/chicc.2017.8028491.

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Hsu, Y. C., James Younger, and S. N. Balakrishnan. "Estimation of Process Noise Covariance in Homing Guidance." In 1991 American Control Conference. IEEE, 1991. http://dx.doi.org/10.23919/acc.1991.4791816.

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Guo, Ruocheng, Jundong Li, and Huan Liu. "INITIATOR: Noise-contrastive Estimation for Marked Temporal Point Process." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/303.

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Copious sequential event data has consistently increased in various high-impact domains such as social media and sharing economy. When events start to take place in a sequential fashion, an important question arises: "what type of event will happen at what time in the near future?" To answer the question, a class of mathematical models called the marked temporal point process is often exploited as it can model the timing and properties of events seamlessly in a joint framework. Recently, various recurrent neural network (RNN) models are proposed to enhance the predictive power of mark temporal
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Reports on the topic "Estimation of the process noise"

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BARUMERLI, Roberto, Piotr MAJDAK, Michele GERONAZZO, David MEIJER, Federico AVANZINI, and Robert BAUMGARTNER. Evaluation of spatial tasks in virtual acoustic environments by means of modeling individual localization performances. Verlag der Österreichischen Akademie der Wissenschaften, 2025. https://doi.org/10.1553/ica_2022_acoustic-environments.

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Virtual acoustic environments (VAEs) are an excellent tool in hearing research, especially in the context of investigating spatial-hearing abilities. On the one hand, the development of VAEs requires a solid evaluation, which can be simplified by applying auditory models. On the other hand, VAE research provides data, which can support the further improvement of auditory models. Here, we describe how Bayesian inference can predict listeners' behavior when estimating the spatial direction of a static sound source presented in a VAE experiment. We show which components of the behavioral process
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Chow, Winston C. Estimation Theory with Fractional Gaussian Noise. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada301443.

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Shen, X., M. Bai, and S. Y. Lee. Noise estimation of beam position monitors at RHIC. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1121843.

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Quijano, Jorge E., Stan E. Dosso, Jan Dettmer, Lisa M. Zurk, and Martin Siderius. Bayesian Ambient Noise Inversion for Geoacoustic Uncertainty Estimation. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada571872.

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Quijano, Jorge E., Stan E. Dosso, Jan Dettmer, Lisa M. Zurk, and Martin Siderius. Bayesian Ambient Noise Inversion for Geoacoustic Uncertainty Estimation. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada575020.

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Wagstaff, Ronald A., and Joal J. Newcomb. Operation Rocky Road: Three-Dimensional Noise Field Directionality Estimation. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada299769.

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Hodgkiss, William S. Source Signature Estimation and Noise Directionality in Shallow Water. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada306524.

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Ait-Sahalia, Yacine, Per Mykland, and Lan Zhang. Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise. National Bureau of Economic Research, 2005. http://dx.doi.org/10.3386/w11380.

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Loh, H. P., Jennifer Lyons, and Charles W. White. Process Equipment Cost Estimation, Final Report. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/797810.

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Lin, Feng-Ling, and Ben H. Cantrell. Bounds on Doppler Frequency Estimation in Correlated Nonstationary Gaussian Noise. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada201052.

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