Robust frequency-hopping spectrum estimation based on sparse bayesian method
This paper considers the problem of estimating multiple frequency hopping signals with unknown hopping pattern. By segmenting the received signals into overlapped measurements and leveraging the property that frequency content at each time instant is intrinsically parsimonious, a sparsity-inspired h...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2015
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| Online Access: | https://hdl.handle.net/10356/107401 http://hdl.handle.net/10220/25471 http://dx.doi.org/10.1109/TWC.2014.2360191 |
| _version_ | 1811692837231656960 |
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| author | Wang, Lu Zhao, Lifan Bi, Guoan Zhang, Liren Zhang, Haijian |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Wang, Lu Zhao, Lifan Bi, Guoan Zhang, Liren Zhang, Haijian |
| author_sort | Wang, Lu |
| collection | NTU |
| description | This paper considers the problem of estimating multiple frequency hopping signals with unknown hopping pattern. By segmenting the received signals into overlapped measurements and leveraging the property that frequency content at each time instant is intrinsically parsimonious, a sparsity-inspired high-resolution time-frequency representation (TFR) is developed to achieve robust estimation. Inspired by the sparse Bayesian learning algorithm, the problem is formulated hierarchically to induce sparsity. In addition to the sparsity, the hopping pattern is exploited via temporal-aware clustering by exerting a dependent Dirichlet process prior over the latent parametric space. The estimation accuracy of the parameters can be greatly improved by this particular information-sharing scheme and sharp boundary of the hopping time estimation is manifested. Moreover, the proposed algorithm is further extended to multi-channel cases, where task-relation is utilized to obtain robust clustering of the latent parameters for better estimation performance. Since the problem is formulated in a full Bayesian framework, labor-intensive parameter tuning process can be avoided. Another superiority of the approach is that high-resolution instantaneous frequency estimation can be directly obtained without further refinement of the TFR. Results of numerical experiments show that the proposed algorithm can achieve superior performance particularly in low signal-to-noise ratio scenarios compared with other recently reported ones. |
| first_indexed | 2024-10-01T06:42:08Z |
| format | Journal Article |
| id | ntu-10356/107401 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:42:08Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | ntu-10356/1074012019-12-06T22:30:12Z Robust frequency-hopping spectrum estimation based on sparse bayesian method Wang, Lu Zhao, Lifan Bi, Guoan Zhang, Liren Zhang, Haijian School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems This paper considers the problem of estimating multiple frequency hopping signals with unknown hopping pattern. By segmenting the received signals into overlapped measurements and leveraging the property that frequency content at each time instant is intrinsically parsimonious, a sparsity-inspired high-resolution time-frequency representation (TFR) is developed to achieve robust estimation. Inspired by the sparse Bayesian learning algorithm, the problem is formulated hierarchically to induce sparsity. In addition to the sparsity, the hopping pattern is exploited via temporal-aware clustering by exerting a dependent Dirichlet process prior over the latent parametric space. The estimation accuracy of the parameters can be greatly improved by this particular information-sharing scheme and sharp boundary of the hopping time estimation is manifested. Moreover, the proposed algorithm is further extended to multi-channel cases, where task-relation is utilized to obtain robust clustering of the latent parameters for better estimation performance. Since the problem is formulated in a full Bayesian framework, labor-intensive parameter tuning process can be avoided. Another superiority of the approach is that high-resolution instantaneous frequency estimation can be directly obtained without further refinement of the TFR. Results of numerical experiments show that the proposed algorithm can achieve superior performance particularly in low signal-to-noise ratio scenarios compared with other recently reported ones. Accepted version 2015-04-30T01:09:19Z 2019-12-06T22:30:12Z 2015-04-30T01:09:19Z 2019-12-06T22:30:12Z 2015 2015 Journal Article Zhao, L., Wang, L., Bi, G., Zhang, L., & Zhang, H. (2015). Robust frequency-hopping spectrum estimation based on sparse bayesian method. IEEE transactions on wireless communications, 14(2), 781-793. 1536-1276 https://hdl.handle.net/10356/107401 http://hdl.handle.net/10220/25471 http://dx.doi.org/10.1109/TWC.2014.2360191 en IEEE transactions on wireless communications © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [Article DOI: http://dx.doi.org/10.1109/TWC.2014.2360191]. 13 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems Wang, Lu Zhao, Lifan Bi, Guoan Zhang, Liren Zhang, Haijian Robust frequency-hopping spectrum estimation based on sparse bayesian method |
| title | Robust frequency-hopping spectrum estimation based on sparse bayesian method |
| title_full | Robust frequency-hopping spectrum estimation based on sparse bayesian method |
| title_fullStr | Robust frequency-hopping spectrum estimation based on sparse bayesian method |
| title_full_unstemmed | Robust frequency-hopping spectrum estimation based on sparse bayesian method |
| title_short | Robust frequency-hopping spectrum estimation based on sparse bayesian method |
| title_sort | robust frequency hopping spectrum estimation based on sparse bayesian method |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems |
| url | https://hdl.handle.net/10356/107401 http://hdl.handle.net/10220/25471 http://dx.doi.org/10.1109/TWC.2014.2360191 |
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