Iterative Sparse Covariance Matrix Fitting Direction of Arrival Estimation Method Based on Vector Hydrophone Array
Aiming at the direction of arrival (DOA) estimation of coherent signals in vector hydrophone array, an iterative sparse covariance matrix fitting algorithm is proposed. Based on the fitting criterion of weighted covariance matrix, the objective function of sparse signal power is constructed, and the...
| Format: | Article |
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| Language: | zho |
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EDP Sciences
2020-02-01
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| Series: | Xibei Gongye Daxue Xuebao |
| Subjects: | |
| Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2020/01/jnwpu2020381p14/jnwpu2020381p14.html |
| _version_ | 1827614623758548992 |
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| collection | DOAJ |
| description | Aiming at the direction of arrival (DOA) estimation of coherent signals in vector hydrophone array, an iterative sparse covariance matrix fitting algorithm is proposed. Based on the fitting criterion of weighted covariance matrix, the objective function of sparse signal power is constructed, and the recursive formula of sparse signal power iteration updating is deduced by using the properties of Frobenius norm. The present algorithm uses the idea of iterative reconstruction to calculate the power of signals on discrete grids, so that the estimated power is more accurate, and thus more accurate DOA estimation can be obtained. The theoretical analysis shows that the power of the signal at the grid point solved by the present algorithm is preprocessed by a filter, which allows signals in specified directions to pass through and attenuate signals in other directions, and has low sensitivity to the correlation of signals. The simulation results show that the average error estimated by the present method is 39.4% of the multi-signal classification high resolution method and 73.7% of the iterative adaptive sparse signal representation method when the signal-to-noise ratio is 15 dB and the non-coherent signal. Moreover, the average error estimated by the present method is 12.9% of the iterative adaptive sparse signal representation method in the case of coherent signal. Therefore, the present algorithm effectively improves the accuracy of target DOA estimation when applying to DOA estimation with highly correlated targets. |
| first_indexed | 2024-03-09T08:57:29Z |
| format | Article |
| id | doaj.art-b6a22d8e15454d13bd89f2fe996f844d |
| institution | Directory Open Access Journal |
| issn | 1000-2758 2609-7125 |
| language | zho |
| last_indexed | 2024-03-09T08:57:29Z |
| publishDate | 2020-02-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Xibei Gongye Daxue Xuebao |
| spelling | doaj.art-b6a22d8e15454d13bd89f2fe996f844d2023-12-02T12:47:13ZzhoEDP SciencesXibei Gongye Daxue Xuebao1000-27582609-71252020-02-01381142310.1051/jnwpu/20203810014jnwpu2020381p14Iterative Sparse Covariance Matrix Fitting Direction of Arrival Estimation Method Based on Vector Hydrophone ArrayAiming at the direction of arrival (DOA) estimation of coherent signals in vector hydrophone array, an iterative sparse covariance matrix fitting algorithm is proposed. Based on the fitting criterion of weighted covariance matrix, the objective function of sparse signal power is constructed, and the recursive formula of sparse signal power iteration updating is deduced by using the properties of Frobenius norm. The present algorithm uses the idea of iterative reconstruction to calculate the power of signals on discrete grids, so that the estimated power is more accurate, and thus more accurate DOA estimation can be obtained. The theoretical analysis shows that the power of the signal at the grid point solved by the present algorithm is preprocessed by a filter, which allows signals in specified directions to pass through and attenuate signals in other directions, and has low sensitivity to the correlation of signals. The simulation results show that the average error estimated by the present method is 39.4% of the multi-signal classification high resolution method and 73.7% of the iterative adaptive sparse signal representation method when the signal-to-noise ratio is 15 dB and the non-coherent signal. Moreover, the average error estimated by the present method is 12.9% of the iterative adaptive sparse signal representation method in the case of coherent signal. Therefore, the present algorithm effectively improves the accuracy of target DOA estimation when applying to DOA estimation with highly correlated targets.https://www.jnwpu.org/articles/jnwpu/full_html/2020/01/jnwpu2020381p14/jnwpu2020381p14.htmlvector hydrophonecoherent sourcesparse covariance matrix fittingdirection of arrival(doa) |
| spellingShingle | Iterative Sparse Covariance Matrix Fitting Direction of Arrival Estimation Method Based on Vector Hydrophone Array Xibei Gongye Daxue Xuebao vector hydrophone coherent source sparse covariance matrix fitting direction of arrival(doa) |
| title | Iterative Sparse Covariance Matrix Fitting Direction of Arrival Estimation Method Based on Vector Hydrophone Array |
| title_full | Iterative Sparse Covariance Matrix Fitting Direction of Arrival Estimation Method Based on Vector Hydrophone Array |
| title_fullStr | Iterative Sparse Covariance Matrix Fitting Direction of Arrival Estimation Method Based on Vector Hydrophone Array |
| title_full_unstemmed | Iterative Sparse Covariance Matrix Fitting Direction of Arrival Estimation Method Based on Vector Hydrophone Array |
| title_short | Iterative Sparse Covariance Matrix Fitting Direction of Arrival Estimation Method Based on Vector Hydrophone Array |
| title_sort | iterative sparse covariance matrix fitting direction of arrival estimation method based on vector hydrophone array |
| topic | vector hydrophone coherent source sparse covariance matrix fitting direction of arrival(doa) |
| url | https://www.jnwpu.org/articles/jnwpu/full_html/2020/01/jnwpu2020381p14/jnwpu2020381p14.html |