Underdetermined DOA Estimation of Off-Grid Sources Based on the Generalized Double Pareto Prior
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913353612394496 |
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| author | Huang, Yongfeng Chen, Zhendong Ye, Kun Zhou, Lang Sun, Haixin |
| author_facet | Huang, Yongfeng Chen, Zhendong Ye, Kun Zhou, Lang Sun, Haixin |
| contents | In this letter, we investigate a new generalized double Pareto based on off-grid sparse Bayesian learning (GDPOGSBL) approach to improve the performance of direction of arrival (DOA) estimation in underdetermined scenarios. The method aims to enhance the sparsity of source signal by utilizing the generalized double Pareto (GDP) prior. Firstly, we employ a first-order linear Taylor expansion to model the real array manifold matrix, and Bayesian inference is utilized to calculate the off-grid error, which mitigates the grid dictionary mismatch problem in underdetermined scenarios. Secondly, an innovative grid refinement method is introduced, treating grid points as iterative parameters to minimize the modeling error between the source and grid points. The numerical simulation results verify the superiority of the proposed strategy, especially when dealing with a coarse grid and few snapshots. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_09554 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Underdetermined DOA Estimation of Off-Grid Sources Based on the Generalized Double Pareto Prior Huang, Yongfeng Chen, Zhendong Ye, Kun Zhou, Lang Sun, Haixin Signal Processing Information Theory In this letter, we investigate a new generalized double Pareto based on off-grid sparse Bayesian learning (GDPOGSBL) approach to improve the performance of direction of arrival (DOA) estimation in underdetermined scenarios. The method aims to enhance the sparsity of source signal by utilizing the generalized double Pareto (GDP) prior. Firstly, we employ a first-order linear Taylor expansion to model the real array manifold matrix, and Bayesian inference is utilized to calculate the off-grid error, which mitigates the grid dictionary mismatch problem in underdetermined scenarios. Secondly, an innovative grid refinement method is introduced, treating grid points as iterative parameters to minimize the modeling error between the source and grid points. The numerical simulation results verify the superiority of the proposed strategy, especially when dealing with a coarse grid and few snapshots. |
| title | Underdetermined DOA Estimation of Off-Grid Sources Based on the Generalized Double Pareto Prior |
| topic | Signal Processing Information Theory |
| url | https://arxiv.org/abs/2405.09554 |