Academic literature on the topic 'Multi-bernoulli'
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Journal articles on the topic "Multi-bernoulli"
Ouyang, C., C. Li, and H. Ji. "Improved multi-target multi-Bernoulli filter." IET Radar, Sonar & Navigation 6, no. 6 (July 1, 2012): 458–64. http://dx.doi.org/10.1049/iet-rsn.2011.0377.
Full textVo, Ba-Tuong, Ba-Ngu Vo, Reza Hoseinnezhad, and Ronald P. S. Mahler. "Robust Multi-Bernoulli Filtering." IEEE Journal of Selected Topics in Signal Processing 7, no. 3 (June 2013): 399–409. http://dx.doi.org/10.1109/jstsp.2013.2252325.
Full textLi, Shijie, and Humin Lei. "Measurement-Driven Multi-Target Multi-Bernoulli Filter." Mathematical Problems in Engineering 2018 (July 22, 2018): 1–9. http://dx.doi.org/10.1155/2018/6515608.
Full textLi, Dong, Chenping Hou, and Dongyun Yi. "Multi-Bernoulli smoother for multi-target tracking." Aerospace Science and Technology 48 (January 2016): 234–45. http://dx.doi.org/10.1016/j.ast.2015.11.017.
Full textSaucan, Augustin-Alexandru, Mark J. Coates, and Michael Rabbat. "A Multisensor Multi-Bernoulli Filter." IEEE Transactions on Signal Processing 65, no. 20 (October 15, 2017): 5495–509. http://dx.doi.org/10.1109/tsp.2017.2723348.
Full textGarcia-Fernandez, Angel F., Lennart Svensson, Jason L. Williams, Yuxuan Xia, and Karl Granstrom. "Trajectory Poisson Multi-Bernoulli Filters." IEEE Transactions on Signal Processing 68 (2020): 4933–45. http://dx.doi.org/10.1109/tsp.2020.3017046.
Full textYUAN, Changshun, Jun WANG, Peng LEI, and Jinping SUN. "Adaptive Multi-Bernoulli Filter Without Need of Prior Birth Multi-Bernoulli Random Finite Set." Chinese Journal of Electronics 27, no. 1 (January 1, 2018): 115–22. http://dx.doi.org/10.1049/cje.2017.10.010.
Full textZhu, Yun, Jun Wang, and Shuang Liang. "Multi-Objective Optimization Based Multi-Bernoulli Sensor Selection for Multi-Target Tracking." Sensors 19, no. 4 (February 25, 2019): 980. http://dx.doi.org/10.3390/s19040980.
Full textZhang, Zijing, Fei Zhang, and Chuantang Ji. "Multi-robot cardinality-balanced multi-Bernoulli filter simultaneous localization and mapping method." Measurement Science and Technology 33, no. 3 (December 23, 2021): 035101. http://dx.doi.org/10.1088/1361-6501/ac3784.
Full textKim, Taekyun, and Dae Kim. "A note on degenerate multi-poly-Bernoulli numbers and polynomials." Applicable Analysis and Discrete Mathematics, no. 00 (2022): 5. http://dx.doi.org/10.2298/aadm200510005k.
Full textDissertations / Theses on the topic "Multi-bernoulli"
Sam, Rosidah. "A novel, flexible, multi-functional handling device based on Bernoulli Principle." Thesis, University of Salford, 2010. http://usir.salford.ac.uk/26891/.
Full textLegrand, Leo. "Contributions aux pistages mono et multi-cibles fondés sur les ensembles finis aléatoires." Thesis, Bordeaux, 2019. http://www.theses.fr/2019BORD0107/document.
Full textDetecting and tracking maritime or ground targets is one of the application fields for surveillance by airborne radar systems. In this specific context, the goal is to estimate the trajectories of one or more moving objects over time by using noisy radar measurements. However, several constraints have to be considered in addition to the problem of estimating trajectories:1. the number of objects inside the region of interest is unknown and may change over time,2. the measurements provided by the radar can arise from the environment and do not necessarily correspond to a mobile object; the phenomenon is called false detection,3. a measurement is not always available for each object; the phenomenon is called non-detection,4. the maneuverability depends on the surface targets.Concerning the three first points, random finite set models can be considered to simultaneously estimate the number of objects and their trajectories in a Bayesian formalism. To deal with the fourth constraint, a classification of the objects to be tracked can be useful. During this PhD thesis, we developped two adaptive approaches that take into account both principles.First of all, we propose a joint target tracking and classification method dedicated to an object with the presence of false detections. Our contribution is to incorporate a filter based on a Bernoulli random finite set. The resulting algorithm combines robustness to the false detections and the ability to classify the object. This classification can exploit the estimation of a discriminating parameter such as the target length that can be deduced from a target length extent measurement.The second adaptive approach presented in this PhD dissertation aims at tracking target groups whose movements are coordinated. Each group is characterized by a common parameter defining the coordination of the movements of its targets. However, the targets keep their own capabilities of maneuvering relatively to the group dynamics. Based on the random finite sets formalism, the proposed solution represents the multi-target multi-group configuration hierarchically. At the top level, the overall situation is modeled by a random finite set whose elements correspond to the target groups. They consist of the common parameter of the group and a multi-target random finite set. The latter contains the state vectors of the targets of the group whose number may change over time. The estimation algorithm developed is also organized hierarchically. A labeled multi-Bernoulli filter (LMB) makes it possible to estimate the number of groups, and for each of them, to obtain their probability of existence as well as their common parameter. For this purpose, the LMB filter interacts with a bank of multi-target filters working conditionally to a group hypothesis. Each multi-target filter estimates the number and state vectors of the objects in the group. This approach provides operational information on the tactical situation
Jacomini, Nelson. "Abordagem analítica para vibrações transversais de vigas multi-segmentadas com seção transversal contínua." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2002. http://hdl.handle.net/10183/118200.
Full textIn this work, it is proposed an analytical approach for the modal determination of various configurations of Euler-Bernoulli bearns with continuous crosssection properties constrained with classical and non-classical boundary conditions. The proposed approach is based on normal conditions for the solutions of the modal equation, yielding a modal formula associated with mathematical and physical properties of the beam. For the mono-segmented case it is not required tbe use of computer for the modal determination. The modes of the various types of beam considered can be expressed in terms of Bessel, triangular and hyperbolic functions. For illustration, it is presented a case of fourth-order polynomial-type flexural stiffness and linear mass.
Pace, Michele. "Stochastic models and methods for multi-object tracking." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2011. http://tel.archives-ouvertes.fr/tel-00651396.
Full textJukic, Miha. "Finite elements for modeling of localized failure in reinforced concrete." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2013. http://tel.archives-ouvertes.fr/tel-00997197.
Full textDona, Marco. "Static and dynamic analysis of multi-cracked beams with local and non-local elasticity." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/14893.
Full textNgo, Quoc hoan. "Double régularisation des polyzêtas en les multi-indices négatifs et extensions rationnelles." Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCD023/document.
Full textIn this memoir are studied the polylogarithms and the harmonic sums at non-positive (i.e. weakly negative) multi-indices. General results about these objects in relation with Hopf algebras are provided. The technics exploited here are based on the combinatorics of non commmutative generating series relative to the Hopf φ−Shuffle algebra. Our work will also propose a global process to renormalize divergent polyzetas. Finally, we will apply these ideas to non-linear dynamical systems with singular inputs
Wu, Chia Huang, and 巫佳煌. "Analysis of Multi-server Queues with Second Optional Service and Bernoulli Vacation." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/12047774190556939008.
Full text國立交通大學
工業工程與管理學系
100
In this dissertation, the optimization investigated multi-server queueing systems with the second optional service (SOS) channel, Bernoulli vacation policy, and customer retrial behaviors are investigated. Multi-server vacation models are more flexible and applicable in practice than single server models. For the multiple server queueing models, the mathematical analyses are complicated and difficult; hence there are only a limited number of studies. All arriving customers need the first essential service (FES) provided by the servers. As soon as the FES of a customer is completed, a customer may leave the system or opt for the SOS. Bernoulli vacation policy means that the server may take one and only one vacation of random length with certain probability at each service completion. As the completion of vacation, the server stays idly for the next new arriving customer or serves the customers waiting in the queue, if any. That is, the single vacation policy. If the customer finding all servers busy always joins the orbit and tries to enter the system for service later. This manner continues until the customer is eventually served then leave the system. This is so-called the customer retrial behaviors. Because most of retrial behaviors of the customers in the orbit are failed without the change of states, we assume that the number of customers who can generate retrial requests is restricted (truncated) to an upper bound value N. This setting makes the mathematical model easier to be analyzed. We investigate four queueing models include the M/M/c (retrial) queue with SOS channel, the M/M/c (retrial) queue with modified Bernoulli single vacation policy, and the M/M/c retrial queue with Bernoulli single vacation policy. For those four queueing systems, we develop the stability conditions and steady-state probability solutions by the matrix-geometric method and recursive technique. Furthermore, it is rather difficult to derive the closed-form solution of the rate matrix for those four queueing systems. The rate matrix is the most important component for implementing the matrix-geometric method to analyze the infinite capacity queueing system. Here, we employ a monotone and convergent sequence to approximate the rate matrix, and obtain the approximation solution of the steady-state probability. The expected cost functions are established to determine the optimal value of the number of servers, mean service rate, mean vacation rate and other system parameters. By implementing the direct search method and Quasi-Newton method, we can find the optimal solution heuristically so that the cost function is minimized. Because of sensitivity investigation on the queueing system with critical input parameters may provide some information for the system analyst. A sensitivity analysis is performed to discuss how the system performances and the optimal solutions are affected by the input parameters in the investigated queueing models. For illustration purpose, numerical results are also presented.
Gong, Xiang. "CONSENSUS ANALYSIS ON NETWORKED MULTI-AGENT SYSTEMS WITH STOCHASTIC COMMUNICATION LINK FAILURE." Thesis, 2013. http://hdl.handle.net/10222/21379.
Full textFANTACCI, CLAUDIO. "Distributed multi-object tracking over sensor networks: a random finite set approach." Doctoral thesis, 2015. http://hdl.handle.net/2158/1003256.
Full textBook chapters on the topic "Multi-bernoulli"
Corcino, Roberto B. "Multi Poly-Bernoulli and Multi Poly-Euler Polynomials." In Applied Mathematical Analysis: Theory, Methods, and Applications, 679–721. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-99918-0_21.
Full textZhang, Yunong, Dechao Chen, and Chengxu Ye. "Multi-Input Bernoulli-Polynomial WASD Neuronet." In Toward Deep Neural Networks, 125–36. Boca Raton, Florida : CRC Press, [2019] | Series: Chapman & Hall/CRC artificial intelligence and robotics series: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429426445-10.
Full textLiu, Rang, Hongqi Fan, and Huaitie Xiao. "A Forward-Backward Labeled Multi-Bernoulli Smoother." In Distributed Computing and Artificial Intelligence, 16th International Conference, 244–52. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23887-2_28.
Full textChen, Junyu, Shiliang Sun, and Jing Zhao. "Multi-label Active Learning with Conditional Bernoulli Mixtures." In Lecture Notes in Computer Science, 954–67. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97304-3_73.
Full textStreit, Roy, Robert Blair Angle, and Murat Efe. "Multi-Bernoulli Mixture and Multiple Hypothesis Tracking Filters." In Analytic Combinatorics for Multiple Object Tracking, 113–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61191-0_5.
Full textZhu, Jihong, Benlian Xu, Mingli Lu, Jian Shi, and Peiyi Zhu. "A Novel Multi-cell Multi-Bernoulli Tracking Method Using Local Fractal Feature Estimation." In Lecture Notes in Computer Science, 312–20. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61833-3_33.
Full textZhang, Xuan, B. John Oommen, and Ole-Christoffer Granmo. "Generalized Bayesian Pursuit: A Novel Scheme for Multi-Armed Bernoulli Bandit Problems." In IFIP Advances in Information and Communication Technology, 122–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23960-1_16.
Full textHoseinnezhad, Reza, Ba-Ngu Vo, and Truong Nguyen Vu. "Visual Tracking of Multiple Targets by Multi-Bernoulli Filtering of Background Subtracted Image Data." In Lecture Notes in Computer Science, 509–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21524-7_63.
Full textDai, Kunpeng, Yafei Wang, Shuai Wang, and Chengliang Yin. "Inter-target Occlusion Handling in Multi-vehicle Corner Tracking Based on Labeled Multi-Bernoulli Filter with Detection Probability Optimization Model." In Lecture Notes in Electrical Engineering, 178–89. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2259-6_16.
Full textDong, Xudong, Xiaofei Zhang, Jun Zhao, Meng Sun, and Jianfeng Li. "DOA Tracking with Multi-Bernoulli Filter for Two-Parallel Linear Array: Reconstruct MUSIC as Pseudo-Likelihood." In Wireless Technology, Intelligent Network Technologies, Smart Services and Applications, 65–73. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5168-7_9.
Full textConference papers on the topic "Multi-bernoulli"
Fontana, Marco, Angel F. Garcia-Fenandez, and Simon Maskell. "Bernoulli merging for the Poisson multi-Bernoulli mixture filter." In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190443.
Full textChen, Yiqi, Ping Wei, Gaiyou Li, Lin Gao, and Yuansheng Li. "The Spline Multi-Target Multi-Bernoulli Filter." In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190412.
Full textVo, Ba-Tuong, and Ba-Ngu Vo. "Multi-Scan Generalized Labeled Multi-Bernoulli Filter." In 2018 21st International Conference on Information Fusion (FUSION 2018). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455419.
Full textGostar, A. K., R. Hoseinnezhad, and A. Bab-Hadiashar. "Multi-bernoulli sensor control for multi-target tracking." In 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2013. http://dx.doi.org/10.1109/issnip.2013.6529808.
Full textYin, Jianjun, Jianqiu Zhang, and Jin Zhao. "The Gaussian Particle multi-target multi-Bernoulli filter." In 2010 2nd International Conference on Advanced Computer Control. IEEE, 2010. http://dx.doi.org/10.1109/icacc.2010.5486859.
Full textLi, Suqi, Wei Yi, Bailu Wang, and Lingjiang Kong. "Computationally Efficient Distributed Multi-Sensor Multi-Bernoulli Filter." In 2018 21st International Conference on Information Fusion (FUSION 2018). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455329.
Full textWilliams, Jason L. "The best fitting multi-Bernoulli filter." In 2014 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2014. http://dx.doi.org/10.1109/ssp.2014.6884615.
Full textYang, Bin, Jun Wang, Wenguang Wang, and Shaoming Wei. "Multipath Generalized Labeled Multi-Bernoulli Filter." In 2018 21st International Conference on Information Fusion (FUSION 2018). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455291.
Full textCheng, Xuan, Hongbing Ji, Yongquan Zhang, and Nanqi Chen. "Box Particle Fast Labeled Multi-Bernoulli Filter." In 2019 IEEE 5th International Conference on Computer and Communications (ICCC). IEEE, 2019. http://dx.doi.org/10.1109/iccc47050.2019.9064190.
Full textKim, Du Yong, and Moongu Jeon. "Robust multi-Bernoulli filtering for visual tracking." In 2014 International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE, 2014. http://dx.doi.org/10.1109/iccais.2014.7020566.
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