Journal articles on the topic 'Space tracking'

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1

Madren, Carrie. "Tracking Turtles from Space." Scientific American 307, no. 1 (June 19, 2012): 27. http://dx.doi.org/10.1038/scientificamerican0712-27.

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2

Danelljan, Martin, Gustav Hager, Fahad Shahbaz Khan, and Michael Felsberg. "Discriminative Scale Space Tracking." IEEE Transactions on Pattern Analysis and Machine Intelligence 39, no. 8 (August 1, 2017): 1561–75. http://dx.doi.org/10.1109/tpami.2016.2609928.

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3

Egeland, Olav. "Task Space Tracking for Manipulators." Modeling, Identification and Control: A Norwegian Research Bulletin 6, no. 2 (1985): 91–101. http://dx.doi.org/10.4173/mic.1985.2.3.

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4

Villagrán de León, Juan Carlos. "Tracking climate change from space." UN Chronicle 46, no. 4 (April 17, 2012): 80–83. http://dx.doi.org/10.18356/a629940b-en.

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5

Krüger, Volker, and Dennis Herzog. "Tracking in object action space." Computer Vision and Image Understanding 117, no. 7 (July 2013): 764–89. http://dx.doi.org/10.1016/j.cviu.2013.02.002.

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6

Stergiou, Chrysovalantis. "Tracking down space and time." Metascience 22, no. 3 (May 15, 2013): 587–90. http://dx.doi.org/10.1007/s11016-013-9800-8.

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7

Aume, Cameron, Keith Andrews, Shantanu Pal, Alice James, Avishkar Seth, and Subhas Mukhopadhyay. "TrackInk: An IoT-Enabled Real-Time Object Tracking System in Space." Sensors 22, no. 2 (January 13, 2022): 608. http://dx.doi.org/10.3390/s22020608.

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Nowadays, there is tremendous growth in the Internet of Things (IoT) applications in our everyday lives. The proliferation of smart devices, sensors technology, and the Internet makes it possible to communicate between the digital and physical world seamlessly for distributed data collection, communication, and processing of several applications dynamically. However, it is a challenging task to monitor and track objects in real-time due to the distinct characteristics of the IoT system, e.g., scalability, mobility, and resource-limited nature of the devices. In this paper, we address the significant issue of IoT object tracking in real time. We propose a system called ‘TrackInk’ to demonstrate our idea. TrackInk will be capable of pointing toward and taking pictures of visible satellites in the night sky, including but not limited to the International Space Station (ISS) or the moon. Data will be collected from sensors to determine the system’s geographical location along with its 3D orientation, allowing for the system to be moved. Additionally, TrackInk will communicate with and send data to ThingSpeak for further cloud-based systems and data analysis. Our proposed system is lightweight, highly scalable, and performs efficiently in a resource-limited environment. We discuss a detailed system’s architecture and show the performance results using a real-world hardware-based experimental setup.
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8

Makin, A. D. J., and T. Chauhan. "Memory-guided tracking through physical space and feature space." Journal of Vision 14, no. 13 (November 14, 2014): 10. http://dx.doi.org/10.1167/14.13.10.

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9

Dirkx, D., R. Noomen, P. N. A. M. Visser, L. I. Gurvits, and L. L. A. Vermeersen. "Space-time dynamics estimation from space mission tracking data." Astronomy & Astrophysics 587 (March 2016): A156. http://dx.doi.org/10.1051/0004-6361/201527524.

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10

Liu Xi-Min, Liu Li-Ren, Sun Jian-Feng, Lang Hai-Tao, Pan Wei-Qing, and Zhao Dong. "Fine tracking in space laser communication." Acta Physica Sinica 54, no. 11 (2005): 5149. http://dx.doi.org/10.7498/aps.54.5149.

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11

Hoots, Felix. "New Space-Based Satellite Tracking Capability." Journal of Guidance, Control, and Dynamics 23, no. 1 (January 2000): 147. http://dx.doi.org/10.2514/2.4501.

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12

Blaser, Erik, Zenon W. Pylyshyn, and Alex O. Holcombe. "Tracking an object through feature space." Nature 408, no. 6809 (November 2000): 196–99. http://dx.doi.org/10.1038/35041567.

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13

Stern, Peter. "Tracking extracellular space in the brain." Science 354, no. 6319 (December 22, 2016): 1547.2–1548. http://dx.doi.org/10.1126/science.354.6319.1547-b.

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14

Alsdorf, D. E. "GEOPHYSICS: Tracking Fresh Water from Space." Science 301, no. 5639 (September 12, 2003): 1491–94. http://dx.doi.org/10.1126/science.1089802.

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15

Egeland, O. "Task-space tracking with redundant manipulators." IEEE Journal on Robotics and Automation 3, no. 5 (October 1987): 471–75. http://dx.doi.org/10.1109/jra.1987.1087118.

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16

Kwei Tu, J. H. Johnson, W. E. Teasdale, D. N. Cravey, Yeng S. Kuo, P. L. Harton, and Yin-Chung Loh. "Space shuttle communications and tracking system." Proceedings of the IEEE 75, no. 3 (1987): 356–70. http://dx.doi.org/10.1109/proc.1987.13742.

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17

Dietz, R. H. "Space station communications and tracking system." Proceedings of the IEEE 75, no. 3 (1987): 371–82. http://dx.doi.org/10.1109/proc.1987.13743.

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18

Kenington, P. B. "Electronic tracking systems for space communications." Electronics & Communications Engineering Journal 2, no. 3 (1990): 95. http://dx.doi.org/10.1049/ecej:19900026.

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19

Jia, Bin, Khanh Pham, Erik Blasch, Genshe Chen, and Dan Shen. "Diffusion-based cooperative space object tracking." Optical Engineering 58, no. 04 (January 21, 2019): 1. http://dx.doi.org/10.1117/1.oe.58.4.041607.

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20

Graham, Andrew. "Tracking the International Space Station (ISS)." Physics Teacher 39, no. 4 (April 2001): 235. http://dx.doi.org/10.1119/1.1543336.

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21

Kwon, Junseok, Ralf Dragon, and Luc Van Gool. "Tracking by switching state space models." Computer Vision and Image Understanding 153 (December 2016): 29–36. http://dx.doi.org/10.1016/j.cviu.2016.03.006.

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22

Jones, Bryony. "Tracking through time and nuclear space." Nature Reviews Genetics 15, no. 8 (July 1, 2014): 516. http://dx.doi.org/10.1038/nrg3783.

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23

Mukherjee, D. P., and S. T. Acton. "Cloud tracking by scale space classification." IEEE Transactions on Geoscience and Remote Sensing 40, no. 2 (2002): 405–15. http://dx.doi.org/10.1109/36.992803.

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24

Ji, Zhanjinag. "Dynamical Property of the Shift Map under Group Action." Advances in Mathematical Physics 2022 (December 22, 2022): 1–5. http://dx.doi.org/10.1155/2022/5969042.

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Firstly, we introduced the concept of G ‐ Lipschitz tracking property, G ‐ asymptotic average tracking property, and G ‐ periodic tracking property. Secondly, we studied their dynamical properties and topological structure and obtained the following conclusions: (1) let X , d be compact metric G ‐ space and the metric d be invariant to G . Then, σ has G ¯ ‐ asymptotic average tracking property; (2) let X , d be compact metric G ‐ space and the metric d be invariant to G . Then, σ has G ¯ ‐ Lipschitz tracking property; (3) let X , d be compact metric G ‐ space and the metric d be invariant to G . Then, σ has G ¯ ‐ periodic tracking property. The above results make up for the lack of theory of G ‐ Lipschitz tracking property, G ‐ asymptotic average tracking property, and G ‐ periodic tracking property in infinite product space under group action.
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25

Qu, Yuanxin, and Bo Cong. "Tracking Ability Evaluation and Calculation of Shipborne Tracking Radar for Space Applications." Journal of Physics: Conference Series 679 (February 29, 2016): 012007. http://dx.doi.org/10.1088/1742-6596/679/1/012007.

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26

Genova, Antonio, and Flavio Petricca. "Deep-Space Navigation with Intersatellite Radio Tracking." Journal of Guidance, Control, and Dynamics 44, no. 5 (May 2021): 1068–79. http://dx.doi.org/10.2514/1.g005610.

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27

Zhou, Zhengshu, Shunya Yamada, Yousuke Watanabe, and Hiroaki Takada. "Tracking Pedestrians Under Occlusion in Parking Space." Computer Systems Science and Engineering 44, no. 3 (2023): 2109–27. http://dx.doi.org/10.32604/csse.2023.029005.

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28

Li, M., J. Li, A. Tamayo, and L. Nan. "MULTIPLE OBJECT TRACKING USING A TRANSFORM SPACE." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2022 (May 18, 2022): 137–43. http://dx.doi.org/10.5194/isprs-annals-v-4-2022-137-2022.

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Abstract. This paper presents a method for multiple object tracking (MOT) in video streams. The method incorporates the prediction of physical locations of people into a tracking-by-detection paradigm. We predict the trajectories of people on an estimated ground plane and apply a learning-based network to extract the appearance features across frames. The method transforms the detected object locations from image space to an estimated ground space to refine the tracking trajectories. This transform space allows the objects detected from multi-view images to be associated under one coordinate system. Besides, the occluded pedestrians in image space can be well separated in a rectified ground plane where the motion models of the pedestrians are estimated. The effectiveness of this method is evaluated on different datasets by extensive comparisons with state-of-the-art techniques. Experimental results show that the proposed method improves MOT tasks in terms of the number of identity switches (IDSW) and the fragmentations (Frag).
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29

Duan Peng, 段朋, 田国会 Tian Guohui, and 张伟 Zhang Wei. "Human Localization and Tracking in Service Space." Chinese Journal of Lasers 41, no. 11 (2014): 1108007. http://dx.doi.org/10.3788/cjl201441.1108007.

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30

Olds, E. S., W. B. Cowan, and P. Jolicoeur. "Tracking visual search over space and time." Psychonomic Bulletin & Review 7, no. 2 (June 2000): 292–300. http://dx.doi.org/10.3758/bf03212984.

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31

Gehly, Steven, Brandon Jones, and Penina Axelrad. "Sensor Allocation for Tracking Geosynchronous Space Objects." Journal of Guidance, Control, and Dynamics 41, no. 1 (January 2018): 149–63. http://dx.doi.org/10.2514/1.g000421.

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32

Showstack, Randy. "Space debris tracking needs improvements, report states." Eos, Transactions American Geophysical Union 93, no. 38 (September 18, 2012): 364. http://dx.doi.org/10.1029/2012eo380007.

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33

Rudd, Jack G., Richard A. Marsh, and Marcus L. Munger. "Large-scale space object tracking using APL2." ACM SIGAPL APL Quote Quad 29, no. 3 (March 1999): 154–63. http://dx.doi.org/10.1145/327600.327639.

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34

Qiang, Ji. "On Long-Term Space-Charge Tracking Simulation." Journal of Physics: Conference Series 1067 (September 2018): 062026. http://dx.doi.org/10.1088/1742-6596/1067/6/062026.

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35

Xu, Haitao. "Tracking Lagrangian trajectories in position–velocity space." Measurement Science and Technology 19, no. 7 (June 17, 2008): 075105. http://dx.doi.org/10.1088/0957-0233/19/7/075105.

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36

Huang, Lianghua, Bo Ma, Jianbing Shen, Hui He, Ling Shao, and Fatih Porikli. "Visual Tracking by Sampling in Part Space." IEEE Transactions on Image Processing 26, no. 12 (December 2017): 5800–5810. http://dx.doi.org/10.1109/tip.2017.2745204.

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37

Braun, J., and A. Pastukhov. "Tracking coherent pattern motion 'through feature space'." Journal of Vision 4, no. 8 (August 1, 2004): 367. http://dx.doi.org/10.1167/4.8.367.

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38

Dionnet, Fabien, and Eric Marchand. "Stereo Tracking and Servoing for Space Applications." Advanced Robotics 23, no. 5 (January 2009): 579–99. http://dx.doi.org/10.1163/156855309x420101.

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39

Cataldo, Davide, Luca Gentile, Selenia Ghio, Elisa Giusti, Sonia Tomei, and Marco Martorella. "Multibistatic Radar for Space Surveillance and Tracking." IEEE Aerospace and Electronic Systems Magazine 35, no. 8 (August 1, 2020): 14–30. http://dx.doi.org/10.1109/maes.2020.2978955.

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40

Larkin, Marilynn. "Tracking the space shuttle on the web." Lancet 357, no. 9268 (May 2001): 1631. http://dx.doi.org/10.1016/s0140-6736(00)04737-1.

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41

Pulvermüller, Friedemann, Yury Shtyrov, Risto J. Ilmoniemi, and William D. Marslen-Wilson. "Tracking speech comprehension in space and time." NeuroImage 31, no. 3 (July 2006): 1297–305. http://dx.doi.org/10.1016/j.neuroimage.2006.01.030.

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42

Hari, Riitta. "Tracking brain functions in space and time." Behavioral and Brain Sciences 18, no. 2 (June 1995): 359–60. http://dx.doi.org/10.1017/s0140525x00038838.

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AbstractThe authors of Images of mind have been highly successful in unravelling the neural basis of complex brain functions. Their emphasis on top-down processingin experimental neuroscience is especially important and, it is hoped, influential. Tracking brain activation accurately botli in space and in time would benefit from studiesofindividual subjects without relying on grand average data.
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43

Zhang, Xiang-yu, Guo-hong Wang, Zhen-yu Song, and Jiao-jiao Gu. "Hypersonic sliding target tracking in near space." Defence Technology 11, no. 4 (December 2015): 370–81. http://dx.doi.org/10.1016/j.dt.2015.05.004.

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44

Faucher, Pascal, Regina Peldszus, and Amélie Gravier. "Operational space surveillance and tracking in Europe." Journal of Space Safety Engineering 7, no. 3 (September 2020): 420–25. http://dx.doi.org/10.1016/j.jsse.2020.07.005.

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45

Xiao-xiang, Zhag, Wu Lian-da, and Xiong Jian-ning. "Circular orbit tracking method of space objects." Chinese Astronomy and Astrophysics 28, no. 1 (January 2004): 94–104. http://dx.doi.org/10.1016/s0275-1062(04)90011-2.

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46

Megret, R., and J. M. Jolion. "Tracking scale-space blobs for video description." IEEE Multimedia 9, no. 2 (2002): 34–43. http://dx.doi.org/10.1109/93.998053.

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47

Li, Shengjie, Shuai Zhao, Bo Cheng, and Junliang Chen. "Efficient Particle Scale Space for Robust Tracking." IEEE Signal Processing Letters 27 (2020): 371–75. http://dx.doi.org/10.1109/lsp.2020.2973098.

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48

Uttam, Shikhar, Nathan A. Goodman, Mark A. Neifeld, Changsoon Kim, Renu John, Jungsang Kim, and David Brady. "Optically multiplexed imaging with superposition space tracking." Optics Express 17, no. 3 (January 29, 2009): 1691. http://dx.doi.org/10.1364/oe.17.001691.

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49

Mao, Zheng, Xiaojun Qu, Fuling Wei, and Yali Wang. "A tracking system with space virtual feedback." Journal of Control Theory and Applications 6, no. 3 (August 2008): 277–80. http://dx.doi.org/10.1007/s11768-008-6037-y.

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50

Phogat, Karmvir Singh, and Dong Eui Chang. "Model Predictive Regulation on Manifolds in Euclidean Space." Sensors 22, no. 14 (July 10, 2022): 5170. http://dx.doi.org/10.3390/s22145170.

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One of the crucial problems in control theory is the tracking of exogenous signals by controlled systems. In general, such exogenous signals are generated by exosystems. These tracking problems are formulated as optimal regulation problems for designing optimal tracking control laws. For such a class of optimal regulation problems, we derive a reduced set of novel Francis–Byrnes–Isidori partial differential equations that achieve output regulation asymptotically and are computationally efficient. Moreover, the optimal regulation for systems on Euclidean space is generalized to systems on manifolds. In the proposed technique, the system dynamics on manifolds is stably embedded into Euclidean space, and an optimal feedback control law is designed by employing well studied, output regulation techniques in Euclidean space. The proposed technique is demonstrated with two representative examples: The quadcopter tracking control and the rigid body tracking control. It is concluded from the numerical studies that the proposed technique achieves output regulation asymptotically in contrast to classical approaches.
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