A reversed visible light multitarget localization system via sparse matrix reconstruction
A reversed indoor multitarget localization system employing compressive sensing (CS) theory is proposed for the first time in terms of visible light positioning (VLP). Unlike conventional VLP systems, where targets process the received light signals to localize themselves, our system works reversely...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/139386 |
| _version_ | 1811685744904765440 |
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| author | Zhang, Ran Zhong, Wen-De Qian, Kemao Zhang, Sheng Du, Pengfei |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Zhang, Ran Zhong, Wen-De Qian, Kemao Zhang, Sheng Du, Pengfei |
| author_sort | Zhang, Ran |
| collection | NTU |
| description | A reversed indoor multitarget localization system employing compressive sensing (CS) theory is proposed for the first time in terms of visible light positioning (VLP). Unlike conventional VLP systems, where targets process the received light signals to localize themselves, our system works reversely by using multiple photodiodes (PDs) mounted on the ceiling to localize mobile targets that carry light emitting diodes. By utilizing its nature of sparsity, the problem of multitarget localization is formulated as a problem of sparse matrix reconstruction, and a 3-step workflow is developed to solve the problem. In this workflow, first, a sensing matrix is redesigned by using QR decomposition to enable CS theory. Next, the conventional l 1 -minimization (l 1 M) algorithm which is highly vulnerable to noise in solving a localization problem is theoretically analyzed and subsequently improved by adopting a reweighted l 1 M approach. Finally, a subgrid localization algorithm is proposed to overcome a common unpractical assumption of on-grid locations, tackle the false peak problem in sparse matrix reconstruction, and ultimately improve the localization precision. The feasibility of our system and supporting algorithms is verified through extensive simulations. Our system demonstrates a good positioning accuracy of 7.4 cm by using 25 PDs when SNR = 20 dB. We also investigate the impact of various factors on the positioning performance, and the obtained results provide an insightful reference paving the way to a practical system design. |
| first_indexed | 2024-10-01T04:49:24Z |
| format | Journal Article |
| id | ntu-10356/139386 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:49:24Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1393862020-05-19T06:07:52Z A reversed visible light multitarget localization system via sparse matrix reconstruction Zhang, Ran Zhong, Wen-De Qian, Kemao Zhang, Sheng Du, Pengfei School of Computer Science and Engineering School of Electrical and Electronic Engineering Interdisciplinary Graduate School (IGS) Engineering::Electrical and electronic engineering Compressive Sensing (CS) Convex Optimization A reversed indoor multitarget localization system employing compressive sensing (CS) theory is proposed for the first time in terms of visible light positioning (VLP). Unlike conventional VLP systems, where targets process the received light signals to localize themselves, our system works reversely by using multiple photodiodes (PDs) mounted on the ceiling to localize mobile targets that carry light emitting diodes. By utilizing its nature of sparsity, the problem of multitarget localization is formulated as a problem of sparse matrix reconstruction, and a 3-step workflow is developed to solve the problem. In this workflow, first, a sensing matrix is redesigned by using QR decomposition to enable CS theory. Next, the conventional l 1 -minimization (l 1 M) algorithm which is highly vulnerable to noise in solving a localization problem is theoretically analyzed and subsequently improved by adopting a reweighted l 1 M approach. Finally, a subgrid localization algorithm is proposed to overcome a common unpractical assumption of on-grid locations, tackle the false peak problem in sparse matrix reconstruction, and ultimately improve the localization precision. The feasibility of our system and supporting algorithms is verified through extensive simulations. Our system demonstrates a good positioning accuracy of 7.4 cm by using 25 PDs when SNR = 20 dB. We also investigate the impact of various factors on the positioning performance, and the obtained results provide an insightful reference paving the way to a practical system design. 2020-05-19T06:07:52Z 2020-05-19T06:07:52Z 2018 Journal Article Zhang, R., Zhong, W.-D., Qian, K., Zhang, S., & Du, P. (2018). A reversed visible light multitarget localization system via sparse matrix reconstruction. IEEE Internet of Things Journal, 5(5), 4223-4230. doi:10.1109/JIOT.2018.2849375 2327-4662 https://hdl.handle.net/10356/139386 10.1109/JIOT.2018.2849375 2-s2.0-85048882008 5 5 4223 4230 en IEEE Internet of Things Journal © 2018 IEEE. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Compressive Sensing (CS) Convex Optimization Zhang, Ran Zhong, Wen-De Qian, Kemao Zhang, Sheng Du, Pengfei A reversed visible light multitarget localization system via sparse matrix reconstruction |
| title | A reversed visible light multitarget localization system via sparse matrix reconstruction |
| title_full | A reversed visible light multitarget localization system via sparse matrix reconstruction |
| title_fullStr | A reversed visible light multitarget localization system via sparse matrix reconstruction |
| title_full_unstemmed | A reversed visible light multitarget localization system via sparse matrix reconstruction |
| title_short | A reversed visible light multitarget localization system via sparse matrix reconstruction |
| title_sort | reversed visible light multitarget localization system via sparse matrix reconstruction |
| topic | Engineering::Electrical and electronic engineering Compressive Sensing (CS) Convex Optimization |
| url | https://hdl.handle.net/10356/139386 |
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