Relevant bibliographies by topics / Recursive Bayesian estimation

Contents

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

Academic literature on the topic 'Recursive Bayesian estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Recursive Bayesian estimation"

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Kárný, Miroslav. "Approximate Bayesian recursive estimation." Information Sciences 285 (November 2014): 100–111. http://dx.doi.org/10.1016/j.ins.2014.01.048.

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Karlsson, R., and F. Gustafsson. "Recursive Bayesian estimation: bearings-only applications." IEE Proceedings - Radar, Sonar and Navigation 152, no. 5 (2005): 305. http://dx.doi.org/10.1049/ip-rsn:20045073.

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Thiemann, M., M. Trosset, H. Gupta, and S. Sorooshian. "Bayesian recursive parameter estimation for hydrologic models." Water Resources Research 37, no. 10 (October 2001): 2521–35. http://dx.doi.org/10.1029/2000wr900405.

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Jemmott, Colin W., and R. Lee Culver. "Recursive Bayesian state estimation for passive sonar localization." Journal of the Acoustical Society of America 127, no. 3 (March 2010): 1960. http://dx.doi.org/10.1121/1.3385007.

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Kramer, Stuart C., and Harold W. Sorenson. "Recursive Bayesian estimation using piece-wise constant approximations." Automatica 24, no. 6 (November 1988): 789–801. http://dx.doi.org/10.1016/0005-1098(88)90055-6.

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Vasudevan, Sathyanarayanan, Richard H. Anderson, Shawn Kraut, Peter Gerstoft, L. Ted Rogers, and Jeffrey L. Krolik. "Recursive Bayesian electromagnetic refractivity estimation from radar sea clutter." Radio Science 42, no. 2 (April 2007): n/a. http://dx.doi.org/10.1029/2005rs003423.

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Anyfantaki, Sofia, and Antonis Demos. "Estimation and Properties of a Time-Varying GQARCH(1,1)-M Model." Journal of Probability and Statistics 2011 (2011): 1–39. http://dx.doi.org/10.1155/2011/718647.

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Time-varying GARCH-M models are commonly used in econometrics and financial economics. Yet the recursive nature of the conditional variance makes exact likelihood analysis of these models computationally infeasible. This paper outlines the issues and suggests to employ a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a simulated Bayesian solution in only computational operations, where is the sample size. Furthermore, the theoretical dynamic properties of a time-varying GQARCH(1,1)-M are derived. We discuss them and apply the suggested Bayesian estimation to three major stock markets.
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Odo, Wataru, Daisuke Kimoto, Makoto Kumon, and Tomonari Furukawa. "Active Sound Source Localization by Pinnae with Recursive Bayesian Estimation." Journal of Robotics and Mechatronics 29, no. 1 (February 20, 2017): 49–58. http://dx.doi.org/10.20965/jrm.2017.p0049.

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[abstFig src='/00290001/05.jpg' width='300' text='Schematic of the proposed system for actively localizing the sound source' ] Animals use two ears to localize the source of a sound, and this paper considers a robot system that localizes a sound source by using two microphones with active external reflectors that mimic movable pinnae. The body of the robot and the environment both affect the propagation of sound waves, which complicates mapping the acoustic cues to the source. The mapping may be multimodal, and the observed acoustic cues may lead to the incorrect estimation of the locations. In order to achieve sound source localization with such multimodal likelihoods, this paper presents a method for determining a configuration of active pinnae, which uses prior knowledge to optimize their location and orientation, and thus attenuates the effects of pseudo-peaks in the observations. The observations are also adversely affected by noise in the sensor signals, and thus Bayesian inference approach to process them is further introduced. Results of experiments that validate the proposed method are also presented.
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Tagade, Piyush, Krishnan S. Hariharan, Priya Gambhire, Subramanya Mayya Kolake, Taewon Song, Dukjin Oh, Taejung Yeo, and Seokgwang Doo. "Recursive Bayesian filtering framework for lithium-ion cell state estimation." Journal of Power Sources 306 (February 2016): 274–88. http://dx.doi.org/10.1016/j.jpowsour.2015.12.012.

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Chen, Yanbo, Feng Liu, Shengwei Mei, Guangyu He, Qiang Lu, and Yanlan Fu. "An Improved Recursive Bayesian Approach for Transformer Tap Position Estimation." IEEE Transactions on Power Systems 28, no. 3 (August 2013): 2830–41. http://dx.doi.org/10.1109/tpwrs.2013.2248761.

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Dissertations / Theses on the topic "Recursive Bayesian estimation"

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Hanif, A. "Computational intelligence sequential Monte Carlos for recursive Bayesian estimation." Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1403732/.

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Recursive Bayesian estimation using sequential Monte Carlos methods is a powerful numerical technique to understand latent dynamics of non-linear non-Gaussian dynamical systems. Classical sequential Monte Carlos suffer from weight degeneracy which is where the number of distinct particles collapse. Traditionally this is addressed by resampling, which effectively replaces high weight particles with many particles with high inter-particle correlation. Frequent resampling, however, leads to a lack of diversity amongst the particle set in a problem known as sample impoverishment. Traditional sequential Monte Carlo methods attempt to resolve this correlated problem however introduce further data processing issues leading to minimal to comparable performance improvements over the sequential Monte Carlo particle filter. A new method, the adaptive path particle filter, is proposed for recursive Bayesian estimation of non-linear non-Gaussian dynamical systems. Our method addresses the weight degeneracy and sample impoverishment problem by embedding a computational intelligence step of adaptive path switching between generations based on maximal likelihood as a fitness function. Preliminary tests on a scalar estimation problem with non-linear non-Gaussian dynamics and a non-stationary observation model and the traditional univariate stochastic volatility problem are presented. Building on these preliminary results, we evaluate our adaptive path particle filter on the stochastic volatility estimation problem. We calibrate the Heston stochastic volatility model employing a Markov chain Monte Carlo on six securities. Finally, we investigate the efficacy of sequential Monte Carlos for recursive Bayesian estimation of astrophysical time series. We posit latent dynamics for both regularized and irregular astrophysical time series, calibrating fifty-five quasar time series using the CAR(1) model. We find the adaptive path particle filter to statistically significantly outperform the standard sequential importance resampling particle filter, the Markov chain Monte Carlo particle filter and, upon Heston model estimation, the particle learning algorithm particle filter. In addition, from our quasar MCMC calibration we find the characteristic timescale τ to be first-order stable in contradiction to the literature though indicative of a unified underlying structure. We offer detailed analysis throughout, and conclude with a discussion and suggestions for future work.
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Baziw, Erick. "Application of Bayesian recursive estimation for seismic signal processing." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/30715.

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Bayesian recursive estimation (BRE) requires that the posterior density function be estimated so that conditional mean estimates of desired parameters or states can be obtained. BRE has been referred to as a complete solution to the estimation problem since the posterior density function embodies all available statistical information (i.e., prior, likelihood and evidence). Until recent advances in BRE, most applications required that the system and measurement equations be linear, and that the process and measurement noise be Gaussian and white. A Kalman filter, KF, (closed form solution to the BRE) could be applied to systems that met these conditions. Previous applications of the KF to solve seismic signal processing problems (e.g., deconvolution) have had very limited success and acceptability in the geophysics signal processing community due to the restrictive nature of the KF. The recently new BRE development of sequential Monte Carlo (SMC) techniques for numerically solving non-stationary and non-linear problems has generated considerable interest and active research within the last decade. This thesis focuses upon the implementation of SMC techniques (e.g., particle filtering) for solving seismic signal processing problems. All the associated filters of BRE (hidden Markov model filter, KF, particle filter, Rao-Blackwellised particle filter, and jump Markov systems) and a new and highly robust and unique model of the seismic source wavelet are implemented in two innovative algorithms for solving the important problems of passive seismic event detection and blind seismic deconvolution. A ground-breaking concept in blind seismic deconvolution referred to as principle phase decomposition (PPD) is outlined and evaluated in this thesis. The PPD technique estimates and separates overlapping source wavelets instead of estimating high bandwidth reflection coefficients. It is shown that one can then easily generate reflection coefficients from the separated source wavelets. In this thesis many advantages of the PPD are outlined. Simulated seismogram data with low signal-to-noise ratios is blindly deconvolved where non-stationary, mixed-phase, and zero-phase source wavelets are present. I believe that there are currently no existing blind seismic deconvolution techniques which could obtain comparable performance results of the PPD technique. The work in this thesis has resulted in three IEEE publications and one peer reviewed conference publication.
Science, Faculty of
Earth, Ocean and Atmospheric Sciences, Department of
Graduate
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Rosén, Olov. "Parallel Stochastic Estimation on Multicore Platforms." Doctoral thesis, Uppsala universitet, Avdelningen för systemteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-246859.

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The main part of this thesis concerns parallelization of recursive Bayesian estimation methods, both linear and nonlinear such. Recursive estimation deals with the problem of extracting information about parameters or states of a dynamical system, given noisy measurements of the system output and plays a central role in signal processing, system identification, and automatic control. Solving the recursive Bayesian estimation problem is known to be computationally expensive, which often makes the methods infeasible in real-time applications and problems of large dimension. As the computational power of the hardware is today increased by adding more processors on a single chip rather than increasing the clock frequency and shrinking the logic circuits, parallelization is one of the most powerful ways of improving the execution time of an algorithm. It has been found in the work of this thesis that several of the optimal filtering methods are suitable for parallel implementation, in certain ranges of problem sizes. For many of the suggested parallelizations, a linear speedup in the number of cores has been achieved providing up to 8 times speedup on a double quad-core computer. As the evolution of the parallel computer architectures is unfolding rapidly, many more processors on the same chip will soon become available. The developed methods do not, of course, scale infinitely, but definitely can exploit and harness some of the computational power of the next generation of parallel platforms, allowing for optimal state estimation in real-time applications.
CoDeR-MP
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Misirli, Baysal Feyzan. "Improving efficiency and effectiveness of Bayesian recursive parameter estimation for hydrologic models." Diss., The University of Arizona, 2003. http://hdl.handle.net/10150/280488.

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There are several sources of uncertainties in hydrologic modeling studies. Conventional deterministic modeling techniques typically ignore most of these uncertainties. However, there has been a growing need for better quantification of the accuracy and precision of hydrologic model predictions. Bayesian Recursive Estimation (BaRE) is an algorithm being developed towards considering these uncertainties for parameter estimation and prediction within an operational setting. This dissertation work evaluated and improved the current version of the algorithm. The methodology was improved using a progressive re-sampling of the Highest Probability Density (HPD) region of the parameter space, which concentrated the samples in the current HPD region while terminating computations in the nonproductive portions of the parameter space, rather than evaluating feasible parameter space based on the initial set of samples. The covariance structure of the well behaving parameter sets is used to generate new parameter sets, resulting in significant improvements compared to the original BaRE. Further, to reduce the "model/data overconfidence" problem, an entropy term and a data lack-of-confidence factor were introduced into the probability-updating rule. Comparison to batch calibration using the popular Shuffled Complex Evolution (SCE-UA) optimization method indicated that the improved recursive calibration technique is a powerful tool, especially useful where basins are recently gauged and hydrologic data are not well accumulated. The final method is also effective in tracing the temporal variations of parameters as a response to natural or human induced changes in the hydrologic system.
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Towler, Jerry Alwynne. "Autonomous Aerial Localization of Radioactive Point Sources via Recursive Bayesian Estimation and Contour Analysis." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/43465.

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The rapid, accurate determination of the positions and strengths of sources of dangerous radioactivity takes high priority after a catastrophic event to ensure the safety of personnel, civilians, and emergency responders. This thesis presents approaches and algorithms to autonomously investigate radioactive material using an unmanned aerial vehicle.
Performing this autonomous analysis comprises five major steps: ingress from a base of operations to the danger zone, initial detection of radioactive material, measurement of the strength of radioactive emissions, analysis of the data to provide position and intensity estimates, and finally egress from the area of interest back to the launch site. In all five steps, time is of critical importance: faster responses promise potentially saved lives.
A time-optimal ingress and egress path planning method solves the first and last steps. Vehicle capabilities and instrument sensitivity inform the development of an efficient search path within the area of interest. Two algorithmsâ a grid-based recursive Bayesian estimator and a novel radiation contour analysis methodâ are presented to estimate the position of radioactive sources using simple gross gamma ray event count data from a nondirectional radiation detector. The latter procedure also correctly estimates the number of sources present and their intensities.
Ultimately, a complete unsupervised mission is developed, requiring minimal initial operator interaction, that provides accurate characterization of the radiation environment of an area of interest as quickly as reasonably possible.
Master of Science
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Lavis, Benjamin Mark Mechanical &amp Manufacturing Engineering Faculty of Engineering UNSW. "Spatially reconfigurable and non-parametric representation of dynamic bayesian beliefs." Publisher:University of New South Wales. Mechanical & Manufacturing Engineering, 2008. http://handle.unsw.edu.au/1959.4/41468.

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This thesis presents a means for representing and computing beliefs in the form of arbitrary probability density functions with a guarantee for the ongoing validity of such beliefs over indefinte time frames. The foremost aspect of this proposal is the introduction of a general, theoretical, solution to the guaranteed state estimation problem from within the recursive Bayesian estimation framework. The solution presented here determines the minimum space required, at each stage of the estimation process, to represent the belief with limited, or no, loss of information. Beyond this purely theoretical aspect, a number of numerical techniques, capable of determining the required space and performing the appropriate spatial reconfiguration, whilst also computing and representing the belief functions, are developed. This includes a new, hybrid particle-element approach to recursive Bayesian estimation. The advantage of spatial reconfiguration as presented here is that it ensures that the belief functions consider all plausible states of the target system, without altering the recursive Bayesian estimation equations used to form those beliefs. Furthermore, spatial reconfiguration as proposed in this dissertation enhances the estimation process since it allows computational resources to be concentrated on only those states considered plausible. Autonomous maritime search and rescue is used as a focus application throughout this dissertation since the searching-and-tracking requirements of the problem involve uncertainty, the use of arbitrary belief functions and dynamic target systems. Nevertheless, the theoretical development in this dissertation has been kept general and independent of an application, and as such the theory and techniques presented here may be applied to any problem involving dynamic Bayesian beliefs. A number of numerical experiments and simulations show the efficacy of the proposed spatially reconfigurable representations, not only in ensuring the validity of the belief functions over indefinite time frames, but also in reducing computation time and improving the accuracy of function approximation. Improvements of an order of magnitude were achieved when compared with traditional, spatially static representations.
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Tong, Xianqiao. "Real-time Prediction of Dynamic Systems Based on Computer Modeling." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/47361.

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This dissertation proposes a novel computer modeling (DTFLOP modeling) technique to predict the real-time behavior of dynamic systems. The proposed DTFLOP modeling classifies the computation into the sequential computation, which is conducted on the CPU, and the parallel computation, which is performed on the GPU and formulates the data transmission between the CPU and the GPU using the parameters of the memory access speed and the floating point operations to be carried out on the CPU and the GPU by relating the calculation rate respectively. With the help of the proposed DTFLOP modeling it is possible to estimate the time cost for computing the model that represents a dynamic system given a certain computer. The proposed DTFLOP modeling can be utilized as a general method to analyze the computation of a model related to a dynamic system and two real life systems are selected to demonstrate its performance, the cooperative autonomous vehicle system and the full-field measurement system. For the cooperative autonomous vehicle system a novel parallel grid-based RBE technique is firstly proposed. The formulations are derived by identifying the parallel computation in the prediction and correction processes of the RBE. A belief fusion technique, which fuses not only the observation information but also the target motion information, has hen been proposed. The proposed DTFLOP modeling is validated using the proposed parallel grid-based RBE technique with the GPU implementation by comparing the estimated time cost with the actual time cost of the parallel grid-based RBE. The superiority of the proposed parallel grid-based RBE technique is investigated by a number of numerical examples in comparison with the conventional grid-based RBE technique. The belief fusion technique is examined by a simulated target search and rescue test and it is observed to maintain more information of the target compared with the conventional observation fusion technique and eventually leads to the better performance of the target search and rescue. For the full-field measurement system a novel parallel DCT full-field measurement technique for measuring the displacement and strain field on the deformed surface of a structure is proposed. The proposed parallel DCT full-field measurement technique measures the displacement and strain field by tracking the centroids of the marked dots on the deformed surface. It identifies and develops the parallel computation in the image analysis and the field estimation processes and then is implemented into the GPU to accelerate the conventional full-field measurement techniques. The detail strategy of the GPU implementation is also developed and presented. The corresponding software package, which also includes a graphic user interface, and the hardware system consist of two digital cameras, LED lights and adjustable support legs to accommodate indoor or outdoor experimental environments are proposed. The proposed DTFLOP modeling is applied to the proposed parallel DCT full-field measurement technique to estimate its performance and the well match with the actual performance demonstrates the DTFLOP modeling. A number of both simulated and real experiments, including the tensile, compressive and bending experiments in the laboratory and outdoor environments, are performed to validate and demonstrate the proposed parallel DCT full-field measurement technique.
Ph. D.
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Takami, Kuya. "Non-Field-of-View Acoustic Target Estimation." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/56892.

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This dissertation proposes a new framework to Non-Field-of-view (NFOV) sound source localization and tracking in indoor environments. The approach takes advantage of sound signal information to localize target position through auditory sensors combination with other sensors within grid-based recursive estimation structure for tracking using nonlinear and non-Gaussian observations. Three approaches to NFOV target localization are investigated. These techniques estimate target positions within the Recursive Bayesian estimation (RBE) framework. The first proposed technique uses a numerical fingerprinting solution based on acoustic cues of a fixed microphone array in a complex indoor environment. The Interaural level differences (ILDs) of microphone pair from a given environment are constructed as an a priori database, and used for calculating the observation likelihood during estimation. The approach was validated in a parametrically controlled testing environment, and followed by real environment validations. The second approach takes advantage of acoustic sensors in combination with an optical sensor to assist target estimation in NFOV conditions. This hybrid of the two sensors constructs observation likelihood through sensor fusion. The third proposed model-based technique localizes the target by taking advantage of wave propagation physics: the properties of sound diffraction and reflection. This approach allows target localization without an a priori knowledge database which is required for the first two proposed techniques. To demonstrate the localization performance of the proposed approach, a series of parameterized numerical and experimental studies were conducted. The validity of the formulation and applicability to the actual environment were confirmed.
Ph. D.
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Steckenrider, John J. "Multi-Bayesian Approach to Stochastic Feature Recognition in the Context of Road Crack Detection and Classification." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/81752.

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This thesis introduces a multi-Bayesian framework for detection and classification of features in environments abundant with error-inducing noise. The approach takes advantage of Bayesian correction and classification in three distinct stages. The corrective scheme described here extracts useful but highly stochastic features from a data source, whether vision-based or otherwise, to aid in higher-level classification. Unlike many conventional methods, these features’ uncertainties are characterized so that test data can be correctively cast into the feature space with probability distribution functions that can be integrated over class decision boundaries created by a quadratic Bayesian classifier. The proposed approach is specifically formulated for road crack detection and characterization, which is one of the potential applications. For test images assessed with this technique, ground truth was estimated accurately and consistently with effective Bayesian correction, showing a 33% improvement in recall rate over standard classification. Application to road cracks demonstrated successful detection and classification in a practical domain. The proposed approach is extremely effective in characterizing highly probabilistic features in noisy environments when several correlated observations are available either from multiple sensors or from data sequentially obtained by a single sensor.
Master of Science
Humans have an outstanding ability to understand things about the world around them. We learn from our youngest years how to make sense of things and perceive our environment even when it is not easy. To do this, we inherently think in terms of probabilities, updating our belief as we gain new information. The methods introduced here allow an autonomous system to think similarly, by applying a fairly common probabilistic technique to the task of perception and classification. In particular, road cracks are observed and classified using these methods, in order to develop an autonomous road condition monitoring system. The results of this research are promising; cracks are identified and correctly categorized with 92% accuracy, and the additional “intelligence” of the system leads to a 33% improvement in road crack assessment. These methods could be applied in a variety of contexts as the leading edge of robotics research seeks to develop more robust and human-like ways of perceiving the world.
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Ndiour, Ibrahima Jacques. "Dynamic curve estimation for visual tracking." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37283.

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This thesis tackles the visual tracking problem as a target contour estimation problem in the face of corrupted measurements. The major aim is to design robust recursive curve filters for accurate contour-based tracking. The state-space representation adopted comprises of a group component and a shape component describing the rigid motion and the non-rigid shape deformation respectively; filtering strategies on each component are then decoupled. The thesis considers two implicit curve descriptors, a classification probability field and the traditional signed distance function, and aims to develop an optimal probabilistic contour observer and locally optimal curve filters. For the former, introducing a novel probabilistic shape description simplifies the filtering problem on the infinite-dimensional space of closed curves to a series of point-wise filtering tasks. The definition and justification of a novel update model suited to the shape space, the derivation of the filtering equations and the relation to Kalman filtering are studied. In addition to the temporal consistency provided by the filtering, extensions involving distributed filtering methods are considered in order to maintain spatial consistency. For the latter, locally optimal closed curve filtering strategies involving curve velocities are explored. The introduction of a local, linear description for planar curve variation and curve uncertainty enables the derivation of a mechanism for estimating the optimal gain associated to the curve filtering process, given quantitative uncertainty levels. Experiments on synthetic and real sequences of images validate the filtering designs.
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Books on the topic "Recursive Bayesian estimation"

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Recursive nonlinear estimation: A geometric approach. Berlin: Springer, 1996.

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Recursive Identification and Parameter Estimation. Taylor & Francis Group, 2014.

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Chen, Hanfu, and Wenxiao Zhao. Recursive Identification and Parameter Estimation. Taylor & Francis Group, 2017.

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Book chapters on the topic "Recursive Bayesian estimation"

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Catlin, Donald E. "Recursive Linear Estimation (Bayesian Estimation)." In Estimation, Control, and the Discrete Kalman Filter, 125–32. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-4528-5_6.

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Eftekhar Azam, Saeed. "Recursive Bayesian Estimation of Partially Observed Dynamic Systems." In Online Damage Detection in Structural Systems, 7–55. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02559-9_2.

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Pardalos, P., V. Yatsenko, and S. Butenko. "Robust Recursive Bayesian Estimation and Quantum Minimax Strategies." In Applied Optimization, 213–32. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/0-306-47536-7_11.

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Moghadamfalahi, Mohammad, Murat Akcakaya, and Deniz Erdogmus. "Active Recursive Bayesian State Estimation for Big Biological Data 1." In Signal Processing and Machine Learning for Biomedical Big Data, 115–32. Boca Raton : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9781351061223-6.

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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, 361–64. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47638-0_39.

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Kárný, Miroslav, and Ivan Nagy. "Recursive Least Squares Approximation of Bayesian Non-Gaussian/Non-Linear Estimation." In Mutual Impact of Computing Power and Control Theory, 123–34. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-2968-2_8.

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Misirli, Feyzan, Hoshin V. Gupta, Soroosh Sorooshian, and Michael Thiemann. "Bayesian recursive estimation of parameter and output uncertainty for watershed models." In Water Science and Application, 113–24. Washington, D. C.: American Geophysical Union, 2003. http://dx.doi.org/10.1029/ws006p0113.

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Astroza, Rodrigo, Hamed Ebrahimian, and Joel P. Conte. "Batch and Recursive Bayesian Estimation Methods for Nonlinear Structural System Identification." In Springer Series in Reliability Engineering, 341–64. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52425-2_15.

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Momani, Mohammad, and Subhash Chall. "Probabilistic Modelling and Recursive Bayesian Estimation of Trust in Wireless Sensor Networks." In Bayesian Network. Sciyo, 2010. http://dx.doi.org/10.5772/10060.

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"Recursive WeissWeinstein Lower Bounds for DiscreteTime Nonlinear Filtering." In Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking. IEEE, 2009. http://dx.doi.org/10.1109/9780470544198.ch71.

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Conference papers on the topic "Recursive Bayesian estimation"

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Suvorova, Sofia, Stephen Howard, and Bill Moran. "Bayesian recursive estimation on the rotation group." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638900.

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Ristic, Branko, and Alfonso Farina. "Recursive Bayesian state estimation from Doppler-shift measurements." In 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2011. http://dx.doi.org/10.1109/issnip.2011.6146626.

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Andersonl, Robert Blake, Mitch Pryor, and Sheldon Landsberger. "Mobile Robotic Radiation Surveying Using Recursive Bayesian Estimation." In 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE). IEEE, 2019. http://dx.doi.org/10.1109/coase.2019.8843064.

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Rust and Jordan. "Recursive Bayesian Estimation Of Effective Defibrillation Shock Intensities." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.595759.

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Rust, L. M., and J. B. Jordan. "Recursive Bayesian estimation of effective defibrillation shock intensities." In 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.5761149.

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Kumon, Makoto, Daisuke Kimoto, Kuya Takami, and Tomonari Furukawa. "Acoustic recursive Bayesian estimation for non-field-of-view targets." In 2013 14th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS). IEEE, 2013. http://dx.doi.org/10.1109/wiamis.2013.6616161.

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Marghi, Yeganeh M., Aziz Kocanaogullari, Murat Akcakaya, and Deniz Erdogmus. "A History-based Stopping Criterion in Recursive Bayesian State Estimation." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683726.

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Bender, Matt J., Hunter M. McClelland, Andrew Kurdila, and Rolf Mueller. "Recursive Bayesian Estimation of Bat Flapping Flight Using Kinematic Trees." In AIAA Modeling and Simulation Technologies Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-0945.

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Xiang, Yijian, Murat Akcakaya, Satyabrata Sen, Deniz Erdogmus, and Arye Nehorai. "Target tracking via recursive Bayesian state estimation in radar networks." In 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017. http://dx.doi.org/10.1109/acssc.2017.8335475.

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Wang, Lan, Jinghao Zheng, Fangfang Chen, and Jisheng Dai. "Sparse Bayesian Learning for DOA Estimation with Recursive Grid-Refining." In 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2018. http://dx.doi.org/10.1109/sam.2018.8448577.

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