Relevant bibliographies by topics / Bayesian filter ; particle filtering ; tracking

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

Academic literature on the topic 'Bayesian filter ; particle filtering ; tracking'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Bayesian filter ; particle filtering ; tracking"

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Hiranmayi, Penumarty, Kola Sai Gowtham, S. Koteswara Rao, and V. Gopi Tilak. "Tracking of pendulum using particle filter with residual resampling." International Journal of Engineering & Technology 7, no. 2.7 (2018): 12. http://dx.doi.org/10.14419/ijet.v7i2.7.10246.

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The phenomenon of simple harmonic motion is more vigilantly explained using a simple pendulum. The angular motion of a pendulum is linear in nature. But the analysis of the motion along the horizontal direction is non-linear. To estimate this, several algorithms like the Kalman filter, Extended Kalman Filter etc. are adopted. Here in this paper, Particle filter is chosen which is a method to form Monte Carlo approximations to the solutions of Bayesian filtering equations. Sequential importance resampling based Particle filters are used where the filtering distributions are multi-nodal or consi
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Tao, Wu, Yong Sheng Xu, and Xiao Yan Wang. "Particle Filtering Algorithm Based on Dynamic Multi-Feature Fusion." Applied Mechanics and Materials 741 (March 2015): 373–77. http://dx.doi.org/10.4028/www.scientific.net/amm.741.373.

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The target tracking technology in image sequence is of great meanings in the military and civilian areas, by using Monte Carlo method to complete the Bayesian recursive, particle filter is widely used in the systems of non-linear and non - Gaussian and good results are gained. However, particle filter there are also disadvantages in terms of sample impoverishment, the choosing of proper proposal distribution, real time and so on. In this paper, the particle filter is utilized to in the feature fusion of the moving target, and the experimental results show that the proposed algorithm has certai
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Closas, Pau, Carles Fernández-Prades, José Diez, and David de Castro. "Nonlinear Bayesian Tracking Loops for Multipath Mitigation." International Journal of Navigation and Observation 2012 (October 17, 2012): 1–15. http://dx.doi.org/10.1155/2012/359128.

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This paper studies Bayesian filtering techniques applied to the design of advanced delay tracking loops in GNSS receivers with multipath mitigation capabilities. The analysis includes tradeoff among realistic propagation channel models and the use of a realistic simulation framework. After establishing the mathematical framework for the design and analysis of tracking loops in the context of GNSS receivers, we propose a filtering technique that implements Rao-Blackwellization of linear states and a particle filter for the nonlinear partition and compare it to traditional delay lock loop/phase
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Han, Yulan, and Chongzhao Han. "A Bayesian Approach to Track Multiple Extended Targets Using Particle Filter for Nonlinear System." Mathematical Problems in Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/7424538.

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To track multiple extended targets for the nonlinear system, this paper employs the idea of the particle filter to track kinematic states and shape formation of extended targets. First, the Bayesian framework is proposed for multiple extended targets to jointly estimate multiple extended target state and association hypothesis. Furthermore, a joint proposal distribution is defined for the multiple extended target state and association hypothesis. Then, the Bayesian framework of multiple extended target tracking is implemented by the particle filtering which could release the high computational
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Jeon, Byunghwan. "Deep Recursive Bayesian Tracking for Fully Automatic Centerline Extraction of Coronary Arteries in CT Images." Sensors 21, no. 18 (2021): 6087. http://dx.doi.org/10.3390/s21186087.

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Extraction of coronary arteries in coronary computed tomography (CT) angiography is a prerequisite for the quantification of coronary lesions. In this study, we propose a tracking method combining a deep convolutional neural network (DNN) and particle filtering method to identify the trajectories from the coronary ostium to each distal end from 3D CT images. The particle filter, as a non-linear approximator, is an appropriate tracking framework for such thin and elongated structures; however, the robust ‘vesselness’ measurement is essential for extracting coronary centerlines. Importantly, we
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Zhong, Lei, Yong Li, Wei Cheng, and Yi Zheng. "Cost-Reference Particle Filter for Cognitive Radar Tracking Systems with Unknown Statistics." Sensors 20, no. 13 (2020): 3669. http://dx.doi.org/10.3390/s20133669.

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A novel robust particle filtering algorithm is proposed for updating both the waveform and noise parameter for tracking accuracy simultaneously and adaptively. The approach is a significant step for cognitive radar towards more robust tracking in random dynamic systems with unknown statistics. Meanwhile, as an intelligent sensor, it would be most desirable for cognitive radar to develop the application of a traditional filter to be adaptive and to expand the adaptation to a wider scope. In this paper, after analysis of the Bayesian bounds and the corresponding cost function design, we propose
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Yardim, Caglar, Peter Gerstoft, and Zoi-Heleni Michalopoulou. "Geophysical signal processing using sequential Bayesian techniques." GEOPHYSICS 78, no. 3 (2013): V87—V100. http://dx.doi.org/10.1190/geo2012-0180.1.

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Sequential Bayesian techniques enable tracking of evolving geophysical parameters via sequential observations. They provide a formulation in which the geophysical parameters that characterize dynamic, nonstationary processes are continuously estimated as new data become available. This is done by using prediction from previous estimates of geophysical parameters, updates stemming from physical and statistical models that relate seismic measurements to the unknown geophysical parameters. In addition, these techniques provide the evolving uncertainty in the estimates in the form of posterior pro
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Yang, Dong He. "Face Tracking Based on Particle Filtering and α-β-γ Filtering". Applied Mechanics and Materials 651-653 (вересень 2014): 2306–9. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.2306.

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In view of the traditional particle filter algorithm cannot guarantee effective tracking in the case of target rotation or obscured. The study proposes a tracking method based on α-β-γ filter and particle filter. The algorithm uses α-β-γ filtering prediction position as the next frame image target candidate model of computing center of particle filter algorithm. The algorithm uses α-β-γ filtering prediction position as the next frame image target candidate model of computing center of particle filter. To reduce the number of iterations of particle filter algorithm, strengthen the real-time tra
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Zhu, Hong Bo, Hai Zhao, Dan Liu, and Chun He Song. "Compressed Iterative Particle Filter for Target Tracking." Applied Mechanics and Materials 55-57 (May 2011): 91–94. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.91.

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Particle filtering has been widely used in the non-linear n-Gaussian target tracking problems. The main problem of particle filtering is the lacking and exhausting of particles, and choosing effective proposed distribution is the key point to overcome it. In this paper, a new mixed particle filtering algorithm was proposed. Firstly, the unscented kalman filtering is used to generate the proposed distribution, and in the resample step, a new certain resample method is used to choose the particles with ordered larger weights. GA algorithm is introduced into the certain resample method to keep th
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Zhan, Ronohui, Qin Xin, and Wan Jianwei. "Modified unscented particle filter for nonlinear Bayesian tracking." Journal of Systems Engineering and Electronics 19, no. 1 (2008): 7–14. http://dx.doi.org/10.1016/s1004-4132(08)60038-9.

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Dissertations / Theses on the topic "Bayesian filter ; particle filtering ; tracking"

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Zhong, Xionghu. "Bayesian framework for multiple acoustic source tracking." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/4752.

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Acoustic source (speaker) tracking in the room environment plays an important role in many speech and audio applications such as multimedia, hearing aids and hands-free speech communication and teleconferencing systems; the position information can be fed into a higher processing stage for high-quality speech acquisition, enhancement of a specific speech signal in the presence of other competing talkers, or keeping a camera focused on the speaker in a video-conferencing scenario. Most of existing systems focus on the single source tracking problem, which assumes one and only one source is acti
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Torstensson, Johan, and Mikael Trieb. "Particle Filtering for Track Before Detect Applications." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-4046.

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<p>Integrated tracking and detection, based on unthresholded measurements, also referred to as track before detect (TBD) is a hard nonlinear and non-Gaussian dynamical estimation and detection problem. However, it is a technique that enables the user to track and detect targets that would be extremely hard to track and detect, if possible at all with ''classical'' methods. TBD enables us to be better able to detect and track weak, stealthy or dim targets in noise and clutter and particles filter have shown to be very useful in the implementation of TBD algorithms. </p><p>This Master's thesis h
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Velmurugan, Rajbabu. "Implementation Strategies for Particle Filter based Target Tracking." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/14611.

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This thesis contributes new algorithms and implementations for particle filter-based target tracking. From an algorithmic perspective, modifications that improve a batch-based acoustic direction-of-arrival (DOA), multi-target, particle filter tracker are presented. The main improvements are reduced execution time and increased robustness to target maneuvers. The key feature of the batch-based tracker is an image template-matching approach that handles data association and clutter in measurements. The particle filter tracker is compared to an extended Kalman filter~(EKF) and a Laplacian filter
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Bradley, Justin Mathew. "Particle Filter Based Mosaicking for Forest Fire Tracking." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2001.pdf.

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Koroglu, Muhammed Taha. "Multiple Hypothesis Testing Approach to Pedestrian Inertial Navigation with Non-recursive Bayesian Map-matching." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1577135195323298.

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Karlsson, Rickard. "Particle filtering for positioning and tracking applications /." Linköping : Dept. of Electrical Engineering, Univ, 2005. http://www.bibl.liu.se/liupubl/disp/disp2005/tek924s.pdf.

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Wu, Jiande. "Parallel Computing of Particle Filtering Algorithms for Target Tracking Applications." ScholarWorks@UNO, 2014. http://scholarworks.uno.edu/td/1953.

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Particle filtering has been a very popular method to solve nonlinear/non-Gaussian state estimation problems for more than twenty years. Particle filters (PFs) have found lots of applications in areas that include nonlinear filtering of noisy signals and data, especially in target tracking. However, implementation of high dimensional PFs in real-time for large-scale problems is a very challenging computational task. Parallel & distributed (P&D) computing is a promising way to deal with the computational challenges of PF methods. The main goal of this dissertation is to develop, implement and ev
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Ozkan, Emre. "Particle Methods For Bayesian Multi-object Tracking And Parameter Estimation." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12610986/index.pdf.

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In this thesis a number of improvements have been established for specific methods which utilize sequential Monte Carlo (SMC), aka. Particle filtering (PF) techniques. The first problem is the Bayesian multi-target tracking (MTT) problem for which we propose the use of non-parametric Bayesian models that are based on time varying extension of Dirichlet process (DP) models. The second problem studied in this thesis is an important application area for the proposed DP based MTT method<br>the tracking of vocal tract resonance frequencies of the speech signals. Lastly, we investigate SMC based par
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Ackerman, Samuel. "A Probabilistic Characterization of Shark Movement Using Location Tracking Data." Diss., Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/499173.

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Statistics<br>Ph.D.<br>Our data consist of measurements of 22 sharks' movements within a 366-acre tidal basin. The measurements are made at irregular time points over a 16-month interval. Constant-length observation intervals would have been desirable, but are often infeasible in practice. We model the sharks' paths at short constant-length intervals by inferring their behavior (feeding vs transiting), interpolating their locations, and estimating parameters of motion (speed and turning angle) in environmental and ecological contexts. We are interested in inferring regional differences in the
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Daniyan, Abdullahi. "Advanced signal processing techniques for multi-target tracking." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/35277.

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The multi-target tracking problem essentially involves the recursive joint estimation of the state of unknown and time-varying number of targets present in a tracking scene, given a series of observations. This problem becomes more challenging because the sequence of observations is noisy and can become corrupted due to miss-detections and false alarms/clutter. Additionally, the detected observations are indistinguishable from clutter. Furthermore, whether the target(s) of interest are point or extended (in terms of spatial extent) poses even more technical challenges. An approach known as ran
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Books on the topic "Bayesian filter ; particle filtering ; tracking"

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Ristic, Branko, Sanjeev Arulampalam, and Neil Gordon. Beyond the Kalman Filter: Particle Filters for Tracking Applications (Artech House Radar Library). Artech House Publishers, 2004.

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Book chapters on the topic "Bayesian filter ; particle filtering ; tracking"

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Dubuisson, Séverine. "Visual Tracking by Particle Filtering." In Tracking with Particle Filter for High-Dimensional Observation and State Spaces. John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119004868.ch1.

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Takeda, Yasuchika, Shinji Fukui, Yuji Iwahori, and Robert J. Woodham. "Detecting Separation of Moving Objects Based on Non-parametric Bayesian Scheme for Tracking by Particle Filter." In Knowledge-Based and Intelligent Information and Engineering Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23866-6_12.

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"A Tutorial on Particle Filters for Online Nonlinear/NonGaussian Bayesian Tracking." In Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking. IEEE, 2009. http://dx.doi.org/10.1109/9780470544198.ch73.

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Ansari, Junaid, Janne Riihijärvi, and Petri Mähönen. "Experiences in Data Processing and Bayesian Filtering Applied to Localization and Tracking in Wireless Sensor Networks." In Localization Algorithms and Strategies for Wireless Sensor Networks. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-396-8.ch016.

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The authors discuss algorithms and solutions for signal processing and filtering for localization and tracking applications in Wireless Sensor Networks. Their focus is on the experiences gained from implementation and deployment of several such systems. In particular, they comment on the data processing solutions found appropriate for commonly used sensor types, and discuss at some length the use of Bayesian filtering for solving the tracking problem. They specifically recommend the use of particle filters as a flexible solution appropriate for tracking in non-linear systems with non-Gaussian measurement errors. They also discuss in detail the design of some of the indoor and outdoor tracking systems they have implemented, highlighting major design decisions and experiences gained from test deployments.
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"The Generalized Monte Carlo Particle Filter." In Bayesian Estimation and Tracking. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118287798.ch17.

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Herbst, Edward P., and Frank Schorfheide. "Particle Filters." In Bayesian Estimation of DSGE Models. Princeton University Press, 2015. http://dx.doi.org/10.23943/princeton/9780691161082.003.0008.

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This chapter explains how the key difficulty that arises when the Bayesian estimation of DSGE models is extended from linear to nonlinear models is the evaluation of the likelihood function, and focuses on the use of particle filters to accomplish this task. The basic bootstrap particle filtering algorithm is remarkably straightforward, but may perform quite poorly in practice. Thus, much of the literature about particle filters focuses on refinements of the bootstrap filter that increases the efficiency of the algorithm. The accuracy of the particle filter can be improved by choosing other proposal distributions. While the tailoring (or adaption) of the proposal distributions tends to require additional computations, the number of particles can often be reduced drastically, which leads to an improvement in efficiency.
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Tung, Tony, and Takashi Matsuyama. "Visual Tracking Using Multimodal Particle Filter." In Computer Vision. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5204-8.ch044.

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Visual tracking of humans or objects in motion is a challenging problem when observed data undergo appearance changes (e.g., due to illumination variations, occlusion, cluttered background, etc.). Moreover, tracking systems are usually initialized with predefined target templates, or trained beforehand using known datasets. Hence, they are not always efficient to detect and track objects whose appearance changes over time. In this paper, we propose a multimodal framework based on particle filtering for visual tracking of objects under challenging conditions (e.g., tracking various human body parts from multiple views). Particularly, the authors integrate various cues such as color, motion and depth in a global formulation. The Earth Mover distance is used to compare color models in a global fashion, and constraints on motion flow features prevent common drifting effects due to error propagation. In addition, the model features an online mechanism that adaptively updates a subspace of multimodal templates to cope with appearance changes. Furthermore, the proposed model is integrated in a practical detection and tracking process, and multiple instances can run in real-time. Experimental results are obtained on challenging real-world videos with poorly textured models and arbitrary non-linear motions.
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"Pointmass filter and CramerRao bound for TerrainAided Navigation." In Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking. IEEE, 2009. http://dx.doi.org/10.1109/9780470544198.ch84.

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Herbst, Edward P., and Frank Schorfheide. "Combining Particle Filters with MH Samplers." In Bayesian Estimation of DSGE Models. Princeton University Press, 2015. http://dx.doi.org/10.23943/princeton/9780691161082.003.0009.

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This chapter argues that in order to conduct Bayesian inference, the approximate likelihood function has to be embedded into a posterior sampler. It begins by combining the particle filtering methods with the MCMC methods, replacing the actual likelihood functions that appear in the formula for the acceptance probability in Algorithm 5 with particle filter approximations. The chapter refers to the resulting algorithm as PFMH algorithm. It is a special case of a larger class of algorithms called particle Markov chain Monte Carlo (PMCMC). The theoretical properties of PMCMC methods were established in Andrieu, Doucet, and Holenstein (2010). Applications of PFMH algorithms in other areas of econometrics are discussed in Flury and Shephard (2011).
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Chebi, Hocine. "Hybrid Attributes Technique Filter for the Tracking of Crowd Behavior." In Advances in Data Mining and Database Management. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6659-6.ch003.

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In this chapter, the authors propose two algorithms based on the device of attributes for tracking of the abnormal behavior of crowd in the visual systems of surveillance. Previous works were realized in the case of detection of behavior, which uses the analysis and the classification of behavior of crowds; this work explores the continuity in the same domain, but in the case of the automatic tracking based on the techniques of filtering one using the KALMAN filter and particles filter. The proposed algorithms he the technique of filter with particle is independent from the detection and from the segmentation human, so is strong with regard to (compared with) the filter of Kalman. In conclusion, the chapter applies the method for tracking of the abnormal behavior to several videos and shows the promising results.
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Conference papers on the topic "Bayesian filter ; particle filtering ; tracking"

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Kim, Taewung, and Hyun-Yong Jeong. "A Crash Prediction Algorithm Using a Particle Filter and Bayesian Decision Theory." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-12118.

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Active safety systems have been developed in automotive industry, and a tracking algorithm and a threat assessment algorithm are needed in such systems to predict the collision between vehicles. It is difficult to track a threat vehicle accurately because of lack of information on a threat vehicle and the measurement noise which does normally not follow Gaussian distribution. Therefore, there is an uncertainty whether the collision will occur or not. Particle filtering is widely used for nonlinear and non-Gaussian tracking problems, and statistical decision theory can be used to make an optima
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Septier, Francois, Sze Kim Pang, Avishy Carmi, and Simon Godsill. "On MCMC-Based particle methods for Bayesian filtering: Application to multitarget tracking." In 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2009). IEEE, 2009. http://dx.doi.org/10.1109/camsap.2009.5413256.

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Godfrey, Gregory A., John Cunningham, and Tuan Tran. "A Bayesian, Nonlinear Particle Filtering Approach for Tracking the State of Terrorist Operations." In 2007 IEEE Intelligence and Security Informatics. IEEE, 2007. http://dx.doi.org/10.1109/isi.2007.379496.

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Ling Wu, Zhidong Deng, and Peifa Jia. "A Post-Resampling Based Particle Filter for Online Bayesian Estimation and Tracking." In 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1713193.

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Delgado-Gonzalo, Ricard, Nicolas Chenouard, and Michael Unser. "A new hybrid Bayesian-variational particle filter with application to mitotic cell tracking." In 2011 8th IEEE International Symposium on Biomedical Imaging (ISBI 2011). IEEE, 2011. http://dx.doi.org/10.1109/isbi.2011.5872784.

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Yu, Miao, Cunjia Liu, Wen-hua Chen, and Jonathon Chambers. "A Bayesian framework with an auxiliary particle filter for GMTI-based ground vehicle tracking aided by domain knowledge." In SPIE Defense + Security, edited by Ivan Kadar. SPIE, 2014. http://dx.doi.org/10.1117/12.2050160.

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Ikoma, N., Y. Miyahara, and H. Maeda. "Tracking of feature points in a scene of moving rigid objects by Bayesian switching structure model with particle filter." In 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718). IEEE, 2003. http://dx.doi.org/10.1109/nnsp.2003.1318071.

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Kadetotad, Sneha, Pramod K. Vemulapalli, Sean N. Brennan, and Constantino Lagoa. "Terrain-Aided Localization Using Feature-Based Particle Filtering." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6025.

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The localization of vehicles on roadways without the use of a GPS has been of great interest in recent years and a number of solutions have been proposed for the same. The localization of vehicles has traditionally been divided by their solution approaches into two different categories: global localization which uses feature-vector matching, and local tracking which has been dealt by using techniques like Particle Filtering or Kalman Filtering. This paper proposes a unifying approach that combines the feature-based robustness of global search with the local tracking capabilities of a particle
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Wang, Peng, Ruqiang Yan, and Robert X. Gao. "Multi-Mode Particle Filter for Bearing Remaining Life Prediction." In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6638.

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As a critical element in rotating machines, remaining useful life (RUL) prediction of rolling bearings plays an essential role in realizing predictive and preventative machine maintenance in modern manufacturing. The physics of defect (e.g. spall) initiation and propagation describes bearing’s service life as generally divided into three stages: normal operation, defect initiation, and accelerated performance degradation. The transition among the stages are embedded in the variations of monitored data, e.g., vibration. This paper presents a multi-mode particle filter (MMPF) that is aimed to: 1
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Romanovas, Michailas, Lasse Klingbeil, Martin Traechtler, and Yiannos Manoli. "Explicit Fractional Model Order Estimation Using Unscented and Ensemble Kalman Filters." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47835.

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The article presents an approach for combining methods of recursive Bayesian estimation with models of dynamical systems with varying differentiation order. The work addresses the problem of explicit fractional order estimation and tracking by constructing an efficient Unscented Kalman filter, where the model order is directly estimated within an augmented state along with the variables of interest. The feasibility of the estimation method is assessed using a benchmark problem based on a simplified fractional neuron firing rate model and time-dependent differentiation order. The proposed techn
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