DOA estimation of array signals based on convolutional sparse autoencoder under sparse prior.
Saved in:
| Title: | DOA estimation of array signals based on convolutional sparse autoencoder under sparse prior. |
|---|---|
| Authors: | REN, Jing1, TAN, Xiuhui1, BAI, Yanping1 baiyp666@163.com, WANG, Peng1, CHENG, Rong1, ZHANG, Feng1, XU, Ting1 |
| Source: | Journal of Measurement Science & Instrumentation. Jun2026, Vol. 17 Issue 2, p254-266. 13p. |
| Subjects: | Direction of arrival estimation, Autoencoders, Mathematical regularization, Deep learning |
| Abstract: | The application of deep learning to direction of arrival (DOA) estimation is of great significance in the field of array signal processing. The use of deep learning for DOA estimation of vector hydrophone array usually directly inputs the covariance matrix of the signal as the signal feature into the network, but this method has limitations such as high data requirements and high computational complexity. This paper proposes a DOA estimation method for vector hydrophone array based on a convolutional sparse autoencoder under sparse prior conditions. This method adds an L1 norm regularization term to the convolutional layer of a convolutional autoencoder to achieve sparsity constraints, and establishes a convolutional sparse autoencoder. At the same time, a residual compensation mechanism is introduced to avoid overfitting and loss of details during the training process. Subsequently, the columns of the signal covariance matrix of the vector hydrophone array are treated as under-sampled noisy linear measurements of the spatial spectrum, and are input into a convolutional sparse autoencoder for feature extraction and reconstruction. Finally, the obtained features are used as inputs for training a convolutional neural network to achieve multi-source DOA estimation. Furthermore, to address the shortcomings of classification methods in off-grid situations, we propose a DOA regression estimation method based on the convolutional sparse autoencoder. The simulation results show that under complex conditions such as low signal-to-noise ratio and a small number of snapshots, the classification method proposed in this paper outperforms various deep learning algorithms and traditional algorithms mentioned in the literature in terms of estimation performance. In addition, the proposed regression method can further improve the DOA estimation performance in off-grid scenarios. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 195003984 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: DOA estimation of array signals based on convolutional sparse autoencoder under sparse prior. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22REN%2C+Jing%22">REN, Jing</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22TAN%2C+Xiuhui%22">TAN, Xiuhui</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22BAI%2C+Yanping%22">BAI, Yanping</searchLink><relatesTo>1</relatesTo><i> baiyp666@163.com</i><br /><searchLink fieldCode="AR" term="%22WANG%2C+Peng%22">WANG, Peng</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22CHENG%2C+Rong%22">CHENG, Rong</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22ZHANG%2C+Feng%22">ZHANG, Feng</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22XU%2C+Ting%22">XU, Ting</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Measurement+Science+%26+Instrumentation%22">Journal of Measurement Science & Instrumentation</searchLink>. Jun2026, Vol. 17 Issue 2, p254-266. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Direction+of+arrival+estimation%22">Direction of arrival estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Autoencoders%22">Autoencoders</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+regularization%22">Mathematical regularization</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The application of deep learning to direction of arrival (DOA) estimation is of great significance in the field of array signal processing. The use of deep learning for DOA estimation of vector hydrophone array usually directly inputs the covariance matrix of the signal as the signal feature into the network, but this method has limitations such as high data requirements and high computational complexity. This paper proposes a DOA estimation method for vector hydrophone array based on a convolutional sparse autoencoder under sparse prior conditions. This method adds an L1 norm regularization term to the convolutional layer of a convolutional autoencoder to achieve sparsity constraints, and establishes a convolutional sparse autoencoder. At the same time, a residual compensation mechanism is introduced to avoid overfitting and loss of details during the training process. Subsequently, the columns of the signal covariance matrix of the vector hydrophone array are treated as under-sampled noisy linear measurements of the spatial spectrum, and are input into a convolutional sparse autoencoder for feature extraction and reconstruction. Finally, the obtained features are used as inputs for training a convolutional neural network to achieve multi-source DOA estimation. Furthermore, to address the shortcomings of classification methods in off-grid situations, we propose a DOA regression estimation method based on the convolutional sparse autoencoder. The simulation results show that under complex conditions such as low signal-to-noise ratio and a small number of snapshots, the classification method proposed in this paper outperforms various deep learning algorithms and traditional algorithms mentioned in the literature in terms of estimation performance. In addition, the proposed regression method can further improve the DOA estimation performance in off-grid scenarios. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=195003984 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.62756/jmsi.1674-8042.2026022 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 254 Subjects: – SubjectFull: Direction of arrival estimation Type: general – SubjectFull: Autoencoders Type: general – SubjectFull: Mathematical regularization Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: DOA estimation of array signals based on convolutional sparse autoencoder under sparse prior. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: REN, Jing – PersonEntity: Name: NameFull: TAN, Xiuhui – PersonEntity: Name: NameFull: BAI, Yanping – PersonEntity: Name: NameFull: WANG, Peng – PersonEntity: Name: NameFull: CHENG, Rong – PersonEntity: Name: NameFull: ZHANG, Feng – PersonEntity: Name: NameFull: XU, Ting IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16748042 Numbering: – Type: volume Value: 17 – Type: issue Value: 2 Titles: – TitleFull: Journal of Measurement Science & Instrumentation Type: main |
| ResultId | 1 |
