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1Academic Journal
المصدر: Tecnura Journal; Vol. 25 No. 67 (2021): January - March; 40-52 ; Tecnura; Vol. 25 Núm. 67 (2021): Enero - Marzo ; 40-52 ; 2248-7638 ; 0123-921X
مصطلحات موضوعية: direction of arrival, sparse reconstruction, compressive sensing, radio localization systems, dirección de llegada, reconstrucción dispersa, sensado comprimido, sistemas de radiolocalización
وصف الملف: text/xml; application/pdf
Relation: https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/16302/17070; https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/16302/16854; Asghar-Sayed, Z. y Ng, B. P. (2019). Aperiodic geometry design for DOA estimation of broadband sources using compressive sensing. Signal Processing, 155, 96-107. DOI: https://doi.org/10.1016/j.sigpro.2018.09.026; Baron, D., Duarte, M. F., Wakin, M. B., Sarvotham, S. y Baraniuk, R. G. (2009). Distributed compressive sensing. Recuperado de http://arxiv.org/abs/0901.3403; Becerra-Mora, Y. A. (2020). Una revisión de plataformas robóticas para el sector de la construcción. Tecnura, 24, 115-132. DOI: https://doi.org/10.14483/22487638.15384 Bougher, B. (2015). Introduction to compressed sensing. Leading Edge, 34(10), 1256-1257. DOI: https://doi.org/10.1190/tle34101256.1; Candès, E. J. y Romberg, J. (2006). Quantitative robust uncertainty principles and optimally sparse decompositions. Foundations of Computational Mathematics, 6(2), 227-254. DOI: https://doi.org/10.1007/s10208-004-0162-x; Candès, E. J., Romberg, J. K. y Tao, T. (2006a). Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 59(8), 1207-1223. DOI: https://doi.org/10.1002/cpa.20124; Candès, E. J., Romberg, J. y Tao, T. (2006b). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2), 489-509. DOI: https://doi.org/10.1109/TIT.2005.862083; Cotter, S. F. (2007). Multiple snapshot matching pursuit for direction of arrival (DOA) estimation. En 2007 15th European Signal Processing Conference (pp. 247-251). Poznán, Polonia.; Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289-1306. DOI: https://doi.org/10.1109/TIT.2006.871582; Duarte, M. F., Sarvotham, S., Baron, D., Wakin, M. B. y Baraniuk, R. G. (2005a). Distributed compressed sensing of jointly sparse signals. En Conference Record - Asilomar Conference on Signals, Systems and Computers, 1537-1541. DOI: https://doi.org/10.1109/acssc.2005.1600024; Duarte, M., Sarvotham, S., Wakin, M., Baron, D. y Baraniuk, R. (2005b). Joint sparsity models for distributed compressed sensing. En Online Proceedings of the Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS).; Eldar, Y. C., Kuppinger, P. y Bölcskei, H. (2010). Block-sparse signals: uncertainty relations and efficient recovery. IEEE Transactions on Signal Processing, 58(6), 3042-3054. DOI: https://doi.org/10.1109/TSP.2010.2044837; Foucart, S. y Rauhut, H. (2013). A mathematical introduction to compressive sensing. Nueva York: Birkhäuser Basel. DOI: https://doi.org/10.1007/978-0-8176-4948-7; Gross, F. (2005). Smart Antennas for wireless communications. McGraw-Hill. https://doi.org/10.1036/007144789X; ross, F. (2005). Smart Antennas for wireless communications. Nueva York: McGraw-Hill. DOI: https://doi.org/10.1036/007144789X; Gu, J. F., Zhu, W. P. y Swamy, M. N. S. (2011). Compressed sensing for DOA estimation with fewer receivers than sensors. En Proceedings - IEEE International Symposium on Circuits and Systems, 1752-1755. DOI: https://doi.org/10.1109/ISCAS.2011.5937922; Gürbüz, A. C., McClellan, J. H. y Cevher, V. (2008). A compressive beamforming method. En ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2617-2620. DOI: https://doi.org/10.1109/ICASSP.2008.4518185; Hincapié, R., Gómez, C., Betancur, L., Lavrenko, A. y Schmitz, J. (2018). Sparse framework for hybrid TDoA/DoA multiple emitter localization. En 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT, 174-179. DOI: https://doi.org/10.1109/ISSPIT.2017.8388637; Jouny, I. (2011). Music DOA estimation with compressive sensing and/or compressive arrays. En IEEE Antennas and Propagation Society, AP-S International Symposium (Digest). DOI: https://doi.org/10.1109/APS.2011.5996902; Lagunas, E., Sharma, S. K., Chatzinotas, S. y Ottersten, B. (2016). Compressive sensing based target counting and localization exploiting joint sparsity. En ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 3231-3235. DOI: https://doi.org/10.1109/ICASSP.2016.7472274; Li, G. y Huang, G. (2014). DOA estimation based on compressive sampling array with novel beamforming. En 2014 31th URSI General Assembly and Scientific Symposium, URSI GASS. DOI: https://doi.org/10.1109/URSIGASS.2014.6929037; Malioutov, D., Çetin, M. y Willsky, A. S. (2005). A sparse signal reconstruction perspective for source localization with sensor arrays. IEEE Transactions on Signal Processing, 53(8), 3010-3022. DOI: https://doi.org/10.1109/TSP.2005.850882; Marín-Alfonso, J., Betancur-Agudelo, L. y Alguello-Fuentes, H. (2017). Further compressio of focal plane array in compressive spectral imaging architectures. Tecnura, 21(52), 45-52. DOI: https://doi.org/10.14483/udistrital.jour.tecnura.2017.2.a03; Martínez-Sarmiento, F. H. y Giral-Ramírez, D. A. (2016). OpenRRArch: una arquitectura abierta, robusta y confiable para el control de robots autónomos. Tecnura, 21(51), 96-104. DOI: Retrieved from https://www.redalyc.org/pdf/2570/257050668007.pdf; Nikitaki, S. y Tsakalides, P. (2011). Localization in wireless networks based on jointly compressed sensing. En 2011 19th European Signal Processing Conference (pp. 1809-1813). Barcelona, España.; Pahlavan, K., Krishnamurthy, P. y Geng, Y. (2015). Localization challenges for the emergence of the smart world. IEEE Access, 3, 3058-3067. DOI: https://doi.org/10.1109/ACCESS.2015.2508648; Paul, A. y Sato, T. (2017). Localization in wireless sensor networks: a survey on algorithms, measurement techniques, applications and challenges. 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المصدر: Tecnura, Volume: 25, Issue: 67, Pages: 40-52, Published: 14 JUL 2021
مصطلحات موضوعية: compressive sensing, sensado comprimido, sistemas de radiolocalización, reconstrucción dispersa, radio localization systems, dirección de llegada, direction of arrival, sparse reconstruction
وصف الملف: text/html
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3Academic Journal
المصدر: Tecnura: Tecnología y Cultura Afirmando el Conocimiento, ISSN 0123-921X, Vol. 25, Nº. 67, 2021, pags. 40-52
مصطلحات موضوعية: dirección de llegada, reconstrucción dispersa, sensado comprimido, sistemas de radiolocalización, direction of arrival, sparse reconstruction, compressive sensing, radio localization systems
وصف الملف: application/pdf
Relation: https://dialnet.unirioja.es/servlet/oaiart?codigo=8026227; (Revista) ISSN 0123-921X