Fast convex optimization method for frequency estimation with prior knowledge in all dimensions
This paper investigates the frequency estimation problem in all dimensions within the recent gridless-sparse-method framework. The frequencies of interest are assumed to follow a prior probability distribution. To effectively and efficiently exploit the prior knowledge, a weighted atomic norm approa...
| 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/10356/141965 |
| _version_ | 1811678193182048256 |
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| author | Yang, Zai Xie, Lihua |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Yang, Zai Xie, Lihua |
| author_sort | Yang, Zai |
| collection | NTU |
| description | This paper investigates the frequency estimation problem in all dimensions within the recent gridless-sparse-method framework. The frequencies of interest are assumed to follow a prior probability distribution. To effectively and efficiently exploit the prior knowledge, a weighted atomic norm approach is proposed in both the 1-D and the multi-dimensional cases. Like the standard atomic norm approach, the resulting optimization problem is formulated as convex programming using the theory of trigonometric polynomials and shares the same computational complexity. Numerical simulations are provided to demonstrate the superior performance of the proposed approach in accuracy and speed compared to the state-of-the-art. |
| first_indexed | 2024-10-01T02:49:22Z |
| format | Journal Article |
| id | ntu-10356/141965 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:49:22Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1419652020-06-12T04:48:46Z Fast convex optimization method for frequency estimation with prior knowledge in all dimensions Yang, Zai Xie, Lihua School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Frequency Estimation Gridless Sparse Methods This paper investigates the frequency estimation problem in all dimensions within the recent gridless-sparse-method framework. The frequencies of interest are assumed to follow a prior probability distribution. To effectively and efficiently exploit the prior knowledge, a weighted atomic norm approach is proposed in both the 1-D and the multi-dimensional cases. Like the standard atomic norm approach, the resulting optimization problem is formulated as convex programming using the theory of trigonometric polynomials and shares the same computational complexity. Numerical simulations are provided to demonstrate the superior performance of the proposed approach in accuracy and speed compared to the state-of-the-art. MOE (Min. of Education, S’pore) 2020-06-12T04:48:46Z 2020-06-12T04:48:46Z 2018 Journal Article Yang, Z., & Xie, L. (2018). Fast convex optimization method for frequency estimation with prior knowledge in all dimensions. Signal Processing, 142, 271-280. doi:10.1016/j.sigpro.2017.07.028 0165-1684 https://hdl.handle.net/10356/141965 10.1016/j.sigpro.2017.07.028 2-s2.0-85026439184 142 271 280 en Signal Processing © 2017 Elsevier B.V. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Frequency Estimation Gridless Sparse Methods Yang, Zai Xie, Lihua Fast convex optimization method for frequency estimation with prior knowledge in all dimensions |
| title | Fast convex optimization method for frequency estimation with prior knowledge in all dimensions |
| title_full | Fast convex optimization method for frequency estimation with prior knowledge in all dimensions |
| title_fullStr | Fast convex optimization method for frequency estimation with prior knowledge in all dimensions |
| title_full_unstemmed | Fast convex optimization method for frequency estimation with prior knowledge in all dimensions |
| title_short | Fast convex optimization method for frequency estimation with prior knowledge in all dimensions |
| title_sort | fast convex optimization method for frequency estimation with prior knowledge in all dimensions |
| topic | Engineering::Electrical and electronic engineering Frequency Estimation Gridless Sparse Methods |
| url | https://hdl.handle.net/10356/141965 |
| work_keys_str_mv | AT yangzai fastconvexoptimizationmethodforfrequencyestimationwithpriorknowledgeinalldimensions AT xielihua fastconvexoptimizationmethodforfrequencyestimationwithpriorknowledgeinalldimensions |