DOA estimation method for sparse arrays based on deep convolutional autoencoder and deep convolutional neural network

This paper proposes a Direction-of-Arrival (DOA) estimation method based on Deep Convolutional Autoencoder (DCAE). This method constructs a DCAE to map the covariance matrix of the received signals of a sparse array into a feature space and then reconstructs it into the covariance matrix of the rece...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Digital signal processing Ročník 168; s. 105627
Hlavní autoři: Guo, Shuhan, Zhang, Qin, Fu, Xiaolong, Zheng, Guimei, Zhou, Hao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.01.2026
Témata:
Convolutional autoencoder
Convolutional neural network
Direction-of-arrival estimation
Sparse array
Sparse array
Direction-of-arrival estimation
Convolutional autoencoder
Convolutional neural network
ISSN:1051-2004
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper proposes a Direction-of-Arrival (DOA) estimation method based on Deep Convolutional Autoencoder (DCAE). This method constructs a DCAE to map the covariance matrix of the received signals of a sparse array into a feature space and then reconstructs it into the covariance matrix of the received signals of a uniform linear array. Subsequently, the DOA estimation is performed in combination with the MUSIC algorithm, which effectively increases the degrees of freedom of the sparse array and better solves the DOA estimation problem under the underdetermined condition of the sparse array. To address the issues of low estimation accuracy and poor angular resolution in traditional algorithms for sparse arrays, a DOA estimation method based on Deep Convolutional Neural Network (DCNN) is proposed. This method extracts the mapping from the covariance matrix of the received signals of the physical elements of the sparse array to the angles of arrival, achieving higher accuracy and higher resolution DOA estimation.
ISSN:1051-2004
DOI:10.1016/j.dsp.2025.105627