Готові списки джерел за темами / Distributed tracking

Добірка наукової літератури з теми "Distributed tracking"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Distributed tracking"

1

Mazzaferri, Javier, Stephane Lefrancois, and Santiago Costantino. "Tracking Inhomogeneously Distributed Particles." Biophysical Journal 106, no. 2 (January 2014): 808a. http://dx.doi.org/10.1016/j.bpj.2013.11.4430.

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2

Kırlı, Dilsun. "Distributed call-tracking for security." Computer Languages, Systems & Structures 28, no. 1 (April 2002): 129–54. http://dx.doi.org/10.1016/s0096-0551(02)00010-3.

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3

Ferramosca, A., D. Limon, I. Alvarado, and E. F. Camacho. "Cooperative distributed MPC for tracking." Automatica 49, no. 4 (April 2013): 906–14. http://dx.doi.org/10.1016/j.automatica.2013.01.019.

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4

Taj, Murtaza, and Andrea Cavallaro. "Distributed and Decentralized Multicamera Tracking." IEEE Signal Processing Magazine 28, no. 3 (May 2011): 46–58. http://dx.doi.org/10.1109/msp.2011.940281.

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5

Coraluppi, S., and C. Carthel. "Distributed tracking in multistatic sonar." IEEE Transactions on Aerospace and Electronic Systems 41, no. 3 (July 2005): 1138–47. http://dx.doi.org/10.1109/taes.2005.1541460.

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6

Ferramosca, A., D. Limon, J. B. Rawlings, and E. F. Camacho. "Cooperative distributed MPC for tracking." IFAC Proceedings Volumes 44, no. 1 (January 2011): 1584–89. http://dx.doi.org/10.3182/20110828-6-it-1002.03581.

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7

Gao, Lin, Giorgio Battistelli, and Luigi Chisci. "Event-Triggered Distributed Multitarget Tracking." IEEE Transactions on Signal and Information Processing over Networks 5, no. 3 (September 2019): 570–84. http://dx.doi.org/10.1109/tsipn.2019.2924196.

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8

Raju, Lakshmi K., Febi Ibrahim, and P. Muralikrishna. "Distributed Target Localization and Tracking Using Distributed Bearing Sensors." Procedia Computer Science 93 (2016): 728–34. http://dx.doi.org/10.1016/j.procs.2016.07.280.

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9

Lee, Hyeon-Seok, and Jae-Jung Yun. "Advanced MPPT Algorithm for Distributed Photovoltaic Systems." Energies 12, no. 18 (September 19, 2019): 3576. http://dx.doi.org/10.3390/en12183576.

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Анотація:
The basic and adaptive maximum power point tracking algorithms have been studied for distributed photovoltaic systems to maximize the energy production of a photovoltaic (PV) module. However, the basic maximum power point tracking algorithms using a fixed step size, such as perturb and observe and incremental conductance, suffer from a trade-off between tracking accuracy and tracking speed. Although the adaptive maximum power point tracking algorithms using a variable step size improve the maximum power point tracking efficiency and dynamic response of the basic algorithms, these algorithms still have the oscillations at the maximum power point, because the variable step size is sensitive to external factors. Therefore, this paper proposes an enhanced maximum power point tracking algorithm that can have fast dynamic response, low oscillations, and high maximum power point tracking efficiency. To achieve these advantages, the proposed maximum power point tracking algorithm uses two methods that can apply the optimal step size to each operating range. In the operating range near the maximum power point, a small fixed step size is used to minimize the oscillations at the maximum power point. In contrast, in the operating range far from the maximum power point, a variable step size proportional to the slope of the power-voltage curve of PV module is used to achieve fast tracking speed under dynamic weather conditions. As a result, the proposed algorithm can achieve higher maximum power point tracking efficiency, faster dynamic response, and lower oscillations than the basic and adaptive algorithms. The theoretical analysis and performance of the proposed algorithm were verified by experimental results. In addition, the comparative experimental results of the proposed algorithm with the other maximum power point tracking algorithms show the superiority of the proposed algorithm.
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10

Song, Taek Lyul, Hyoung Won Kim, and Darko Musicki. "Distributed (nonlinear) target tracking in clutter." IEEE Transactions on Aerospace and Electronic Systems 51, no. 1 (January 2015): 654–68. http://dx.doi.org/10.1109/taes.2014.130151.

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Дисертації з теми "Distributed tracking"

1

Katragadda, Sandeep. "Distributed target tracking in wireless camera networks." Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/25899.

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Distributed target tracking (DTT) is desirable in wireless camera networks to achieve scalability and robustness to node or link failures. DTT estimates the target state via information exchange and fusion among cameras. This thesis proposes new DTT algorithms to handle five major challenges of DTT in wireless camera networks, namely non-linearity in the camera measurement model, temporary lack of measurements (benightedness) due to limited field of view, redundant information in the network, limited connectivity of the network due to limited communication ranges and asynchronous information caused by varying and unknown frame processing delays. The algorithms consist of two phases, namely estimation and fusion. In the estimation phase, the cameras process their captured frames, detect the target, and estimate the target state (location and velocity) and its uncertainty using the Extended Information Filter (EIF) that handles non-linearity. In the fusion phase, the cameras exchange their local target information with their communicative neighbours and fuse the information. The contributions of this thesis are as follows. The target states estimated by the EIFs undergo weighted fusion. The weights are chosen based on the estimated uncertainty (error covariance) and the number of nodes with redundant information such that the information of benighted nodes and the redundant information get lower weights. At each time step, only the cameras having the view of the target and the cameras that might have the view of the target in the next time step participate in the fusion (tracking). This reduces the energy consumption of the network. The algorithm selects the cameras dynamically by using a threshold on their shortest distances (in the communication graph) from the cameras having the view of the target. Before fusion, each camera predicts the target information of other cameras to temporally align its information with the (asynchronous) information received from other cameras. The algorithm predicts the target state using the latest estimated velocity of the target. The experimental results show that the proposed algorithms achieve higher tracking accuracy than the state of the art under the five DTT challenges.
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2

Sitta, Alessandro. "Privacy-Preserving Distributed Optimization via Obfuscated Gradient Tracking." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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As the modern world becomes increasingly digitized and interconnected, distributed systems have proven to be effective in the processing of large volumes of data. In this context, optimization techniques have become essential in an extensive range of domains. However, a major concern, regarding the privacy issue in handling sensitive data, has recently emerged. To address this privacy issue we propose a novel consensus-based privacy-preserving distributed optimization algorithm called Obfuscated Gradient Tracking. The algorithm is characterized by a balanced noise insertion method which protects private data from being revealed to others, while not affecting the result’s accuracy. Indeed, we theoretically prove that the introduced perturbations do not condition the convergence properties of the algorithm, which is proven to reach the optimal solution without compromises. Moreover, security against the widely-used honest-but-curious adversary model, is shown. Furthermore, numerical tests are performed to show the effectiveness of the novel algorithm, both in terms of privacy and convergence properties. Numerical results highlight the Obfuscated Gradient Tracking attractiveness, against standard distributed algorithms, when privacy issues are involved. Finally, we present a privacy-preserving distributed Deep Learning application developed using our novel algorithm, with the aim of demonstrating its general applicability.
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3

Ali, Andreas Mantik. "Distributed acoustic localization and tracking design and analysis." Diss., Restricted to subscribing institutions, 2010. http://proquest.umi.com/pqdweb?did=2023768071&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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4

Ing, Garrick. "Distributed particle filters for object tracking in sensor networks." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98971.

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A particle filter (PF) is a simulation-based algorithm used to solve estimation problems, such as object tracking. The PF works by maintaining a set of "particles" as candidate state descriptions of an object's position. The filter determines how well the set of particles describe the observations and fit the dynamic model, in order to form an object state estimate. The drawback of the basic PF is that the algorithm functions by collecting all data at a fusion centre. This leads to high communication and energy costs in a resource-limited network such as the sensor network. In this thesis, we analyze the PF to determine how it can be modified for efficient use in a sensor network. Our main priority is to keep communication and energy costs low since this increases the network lifetime. We propose two innovative particle filtering algorithms which minimizes the associated costs.
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5

Hoefener, Carl E. "DISTRIBUTED INTERACTIVE SIMULATION PROVEN IN FLIGHT TEST AIRCRAFT TRACKING." International Foundation for Telemetering, 1994. http://hdl.handle.net/10150/608558.

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International Telemetering Conference Proceedings / October 17-20, 1994 / Town & Country Hotel and Conference Center, San Diego, California
With the decline in military budgets worldwide, the need for ever more effective training is becoming apparent. The size of the armed forces is being reduced, so the remaining members have to be trained to be more effective and efficient in doing their jobs. To improve effectiveness and efficiency of performance, military training needs will be increased. Our largest training costs will be in pilot training because of the high cost of keeping aircraft in the air. When we look at the cost of training a pilot to operate in a multiplayer scenario against a large number of unfriendly aircraft and a large number of ground threats, the cost of training is tremendous. It requires a large number of personnel, aircraft and ground equipment to train a limited number of pilots. Our current Red Flag exercises can require as many as 75 aircraft in the air simultaneously plus a large number of ground threats and a large number of support personnel. This can amount to a prohibitive cost to train a limited number of pilots.
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6

Dias, Stiven Schwanz. "Collaborative emitter tracking using distributed sequential Monte Carlo methods." Instituto Tecnológico de Aeronáutica, 2014. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=3137.

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We introduce in this Thesis several particle filter (PF) solutions to the problem of collaborative emitter tracking. In the studied scenario, multiple agents with sensing, processing and communication capabilities passively collect received-signal-strength (RSS) measurements of the same signal originating from a non-cooperative emitter and collaborate to estimate its hidden state. Assuming unknown sensor noise variances, we derive an exact decentralized implementation of the optimal centralized PF solution for this problem in a fully connected network. Next, assuming local internode communication only, we derive two fully distributed consensus-based solutions to the problem using respectively average consensus iterations and a novel ordered minimum consensus approach which allow us to reproduce the exact centralized solution in a finite number of consensus iterations. In the sequel, to reduce the communication cost, we derive a suboptimal tracker which employs suitable parametric approximations to summarize messages that are broadcast over the network. Moreover, to further reduce communication and processing requirements, we introduce a non-iterative tracker based on random information dissemination which is suited for online applications. We derive the proposed random exchange diffusion PF (ReDif-PF) assuming both that observation model parameters are perfectly known and that the emitter is always present. We extend then the ReDif-PF tracker to operate in scenarios with unknown sensor noise variances and propose the Rao-Blackwellized (RB) ReDif-PF. Finally, we introduce the random exchange diffusion Bernoulli filter (RndEx-BF) which enables the network of collaborative RSS sensors to jointly detect and track the emitter within the surveillance space.
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7

Srinivasan, Sabeshan. "Object Tracking in Distributed Video Networks Using Multi-Dimentional Signatures." Fogler Library, University of Maine, 2006. http://www.library.umaine.edu/theses/pdf/SrinivasanSX2006.pdf.

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8

Nguyen, Trang. "Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking." ScholarWorks@UNO, 2003. http://scholarworks.uno.edu/td/20.

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Nonlinear filtering is certainly very important in estimation since most real-world problems are nonlinear. Recently a considerable progress in the nonlinear filtering theory has been made in the area of the sampling-based methods, including both random (Monte Carlo) and deterministic (quasi-Monte Carlo) sampling, and their combination. This work considers the problem of tracking a maneuvering target in a multisensor environment. A novel scheme for distributed tracking is employed that utilizes a nonlinear target model and estimates from local (sensor-based) estimators. The resulting estimation problem is highly nonlinear and thus quite challenging. In order to evaluate the performance capabilities of the architecture considered, advanced sampling-based nonlinear filters are implemented: particle filter (PF), unscented Kalman filter (UKF), and unscented particle filter (UPF). Results from extensive Monte Carlo simulations using different configurations of these algorithms are obtained to compare their effectiveness for solving the distributed target tracking problem.
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9

Krout, David Wayne. "Intelligent ping sequencing for multiple target tracking in distributed sensor fields /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/6045.

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10

Thull, Daniel [Verfasser]. "Tracking Control of Mechanical Distributed Parameter Systems with Applications / Daniel Thull." Aachen : Shaker, 2010. http://d-nb.info/112086397X/34.

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Книги з теми "Distributed tracking"

1

Chen, Fei, and Wei Ren. Distributed Average Tracking in Multi-agent Systems. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39536-0.

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2

Iqbal, R. Noise analysis in tracking the features of a spatially distributed field. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1994.

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3

Edwards, J. B. Tracking the features of a spatially distributed continuous field: (the idealised 2D,deterministic case). Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1994.

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4

Information, Decision, and Control (2007 Adelaide, Australia). 2007 Information Decision and Control (IDC): Adelaide, Australia, 12-14 February 2007. Piscataway, NJ: IEEE, 2007.

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5

Information, Decision, and Control (2007 Adelaide, Aust.). 2007 Information Decision and Control (IDC): Adelaide, Australia, 12-14 February 2007. Piscataway, NJ: IEEE, 2007.

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6

Information, Decision, and Control (2007 Adelaide, Aust.). 2007 Information Decision and Control (IDC): Adelaide, Australia, 12-14 February 2007. Piscataway, NJ: IEEE, 2007.

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7

Information, Decision, and Control (2007 Adelaide, Aust.). 2007 Information Decision and Control (IDC): Adelaide, Australia, 12-14 February 2007. Piscataway, NJ: IEEE, 2007.

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8

Chen, Fei, and Wei Ren. Distributed Average Tracking in Multi-Agent Systems. Springer International Publishing AG, 2021.

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9

Chen, Fei, and Wei Ren. Distributed Average Tracking in Multi-agent Systems. Springer, 2020.

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10

Kukliansky, Alexander. Tracking procedure for non-normally distributed measurement errors. 1987.

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Частини книг з теми "Distributed tracking"

1

Kirli, Zeliha Dilsun. "Distributed Call-Tracking." In Advances in Information Security, 53–74. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1007-9_3.

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2

Chen, Fei, and Wei Ren. "Distributed Average Tracking in Distributed Convex Optimization." In Distributed Average Tracking in Multi-agent Systems, 193–231. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39536-0_10.

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3

Yang, Ming-Hsuan, and Jeffrey Ho. "Toward Robust Online Visual Tracking." In Distributed Video Sensor Networks, 119–36. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-127-1_8.

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4

Garofalakis, Minos. "Tracking Queries over Distributed Streams." In Data-Centric Systems and Applications, 301–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-540-28608-0_15.

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5

Li, Lin, Xiao Li, Anna Scaglione, and Jonathan H. Manton. "Decentralized Subspace Tracking via Gossiping." In Distributed Computing in Sensor Systems, 130–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13651-1_10.

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6

Wang, Zixuan, and Hamid Aghajan. "Tracking by Detection Algorithms Using Multiple Cameras." In Distributed Embedded Smart Cameras, 175–88. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7705-1_8.

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7

Chen, Fei, and Wei Ren. "Distributed Average Tracking via Nonsmooth Feedback." In Distributed Average Tracking in Multi-agent Systems, 39–60. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39536-0_3.

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Chen, Fei, and Wei Ren. "Distributed Average Tracking with Input Saturation." In Distributed Average Tracking in Multi-agent Systems, 157–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39536-0_8.

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9

Chen, Fei, and Wei Ren. "Distributed Average Tracking in Formation Control." In Distributed Average Tracking in Multi-agent Systems, 179–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39536-0_9.

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Karol, Alankar, and Mary-Anne Williams. "Distributed Sensor Fusion for Object Tracking." In RoboCup 2005: Robot Soccer World Cup IX, 504–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11780519_47.

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Тези доповідей конференцій з теми "Distributed tracking"

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Tang, Mingwang, Feifei Li, and Yufei Tao. "Distributed Online Tracking." In SIGMOD/PODS'15: International Conference on Management of Data. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2723372.2737790.

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Severinson, Albin, Eirik Rosnes, and Alexandre Graell i Amat. "Coded Distributed Tracking." In GLOBECOM 2019 - 2019 IEEE Global Communications Conference. IEEE, 2019. http://dx.doi.org/10.1109/globecom38437.2019.9013446.

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3

Chong, Chee-Yee, Kuo-Chu Chang, and Shozo Mori. "Distributed Tracking in Distributed Sensor Networks." In 1986 American Control Conference. IEEE, 1986. http://dx.doi.org/10.23919/acc.1986.4789229.

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Kopsinis, Yannis, Symeon Chouvardas, and Sergios Theodoridis. "Distributed robust subspace tracking." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362841.

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Wolf, Wayne, Senem Velipasalar, Jason Schlessman, Cheng-Yao Chen, and Chang-Hong Lin. "Real-Time Distributed Tracking." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.367338.

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WU, Hao, Junhao Gan, and Rui Zhang. "Learning Based Distributed Tracking." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3403255.

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Maskell, S. R., K. R. Weekes, and M. Briers. "Distributed tracking of stealthy targets using particle filters." In IEE Seminar on Target Tracking: Algorithms and Applications. IEE, 2006. http://dx.doi.org/10.1049/ic:20060553.

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Marian, T., Osnat Mokryn, and Y. Shavitt. "A distributed cooperative target tracking." In 2010 IEEE 26th Convention of Electrical & Electronics Engineers in Israel (IEEEI 2010). IEEE, 2010. http://dx.doi.org/10.1109/eeei.2010.5661935.

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Hoffmann, Martin, Michael Wittke, Yvonne Bernard, Ramin Soleymani, and Jorg Hahner. "DMCtrac: Distributed multi camera tracking." In 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC). IEEE, 2008. http://dx.doi.org/10.1109/icdsc.2008.4635684.

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Zhang, Ying, Henry Leung, Titus K. Y. Lo, and John Litva. "Distributed multisensor multitarget tracking algorithm." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by Ivan Kadar and Vibeke Libby. SPIE, 1995. http://dx.doi.org/10.1117/12.213010.

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Звіти організацій з теми "Distributed tracking"

1

Pao, Lucy Y. Distributed Multisensor Fusion Algorithms for Tracking Applications. Fort Belvoir, VA: Defense Technical Information Center, May 2000. http://dx.doi.org/10.21236/ada377900.

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Esterline, Albert. Benchmark Intelligent Agent Systems for Distributed Battle Tracking. Fort Belvoir, VA: Defense Technical Information Center, June 2008. http://dx.doi.org/10.21236/ada501038.

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Tannenbaum, Allen R. Distributed Systems for Problems in Robust Control and Visual Tracking. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada387787.

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Pattengale, Nicholas, and David Farley. Prototype Distributed Ledger Technology of UF6 Cylinder Tracking in Ethereum. Office of Scientific and Technical Information (OSTI), February 2021. http://dx.doi.org/10.2172/1770508.

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Ceruti, Marion G., and Dwight R. Wilcox. Sensor Ontology Integration for the Knowledge Management for Distributed-Tracking (KMDT) Program. Fort Belvoir, VA: Defense Technical Information Center, June 2006. http://dx.doi.org/10.21236/ada463094.

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Zeger, Kenneth, and Laurence Milstein. Cooperative Communication for Tracking and Surveillance Using Multiple Related Observations and Distributed Transmitters. Fort Belvoir, VA: Defense Technical Information Center, December 2010. http://dx.doi.org/10.21236/ada550358.

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Fujimoto, Richard, Michael Hunter, and Haesun Park. Dynamic Systems for Individual Tracking via Heterogeneous Information Integration and Crowd Source Distributed Simulation. Fort Belvoir, VA: Defense Technical Information Center, December 2015. http://dx.doi.org/10.21236/ad1004753.

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Barbose, Galen L., Naim R. Darghouth, Kristina H. LaCommare, Dev Millstein, and Joe Rand. Tracking the Sun: Installed Price Trends for Distributed Photovoltaic Systems in the United States - 2018 Edition. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1477384.

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Barbose, Galen, Naim Darghouth, Salma Elmallah, Sydney Forrester, Kristina S. H., Kristina, Dev Millstein, Joseph Rand, Will Cotton, Stacy Sherwood, and Eric O'Shaughnessy. Tracking the Sun: Pricing and Design Trends for Distributed Photovoltaic Systems in the United States - 2019 Edition. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1574343.

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Barbose, Galen, Naim Darghouth, Eric O'Shaughnessy, and Sydney Forrester. Tracking the Sun: Pricing and Design Trends for Distributed Photovoltaic Systems in the United States, 2022 Edition. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1887207.

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