Abstract
In array signal processing, the direction of arrival (DOA) of the signal source has drawn broad research interests with its wide applications in fields such as sonar, radar, communications, medical detection, and electronic countermeasures. In recent years, the application of deep learning (DL) to DOA estimation has achieved great success. This study provides a systematic review of research on DOA estimation using deep neural network methods. We manually selected twenty-five papers related to this research from five prominent databases (SpringerLink, IEEE Xplore, ScienceDirect, Scopus, and Google Scholar) for exploration. Six questions describing the overall trend of DOA estimation using deep learning are put forward. Then, we answered these questions by reviewing the literature. This review is helpful for researchers in this field because it provides more specific and comprehensive information needed for future research. Specifically, we first analyzed the background of the selected papers, including the type of publication, the number of citations, and the country of origin. Then, the DL technology used in DOA estimation is systematically analyzed, including the purpose of using DL in DOA estimation, various DL models (convolutional neural network, deep neural network, and combination network), and various DOA estimation schemes. Finally, various evaluation criteria (root-mean-squared error, accuracy, and mean absolute error) are used to evaluate the DL technology in DOA estimation, and various factors (signal-to-noise ratio, number of snapshots, number of antennas, and number of signal sources) affecting DOA estimation are analyzed. Based on our findings, we believe that deep learning can perform DOA estimation well, and there is still room for improvement in deep learning technology. In this study, the factors affecting DOA estimation can be used as the direction for researchers to conduct in-depth research.
Highlights
direction of arrival (DOA) estimation has its origins in the conventional beamforming (CBF) [1], which directly corresponds to the traditional Fourier spectrum estimation method from the time domain signal processing method to the spatial domain signal processing, such that the array angle resolution is restricted by the Rayleigh limit constraint. e Rayleigh limit means that the two signals can be distinguished only when the angular separation between the two far-field signal sources is greater than the antenna beamwidth. e Capon method [2] can minimize the output energy in the interference direction while keeping the output energy in the desired direction constant. is method does not require the number of sources in advance and is robust, but its resolution is not high enough
Study Identification and Selection. e searched phrases are divided into two groups: DOA estimation and deep learning. e string is defined as any term related to signal processing (e.g., “DOA estimation,” and “source number estimation,” “source number enumeration,” and “direction of arrival,” combined with the function OR) with any term related to deep learning (e.g., “artificial neural network,” “human convolutional neural network,” “deep learning,” “Convolutional neural networks (CNNs),” “DNN,” and “Recurrent neural networks (RNNs)”). e search platform chosen were EZAccess Portal (Malaysia Putra University Library Database) and Google Scholar. e former portal contains many well-known databases, namely, SpringerLink, IEEE Xplore, ScienceDirect, and Scopus. e latter portal has a wide range of academic literature, making it easier to search
E root-mean-squared error is used to measure the deviation between the observed value and the true value. e average absolute error can avoid the mutual cancellation of errors, so it can accurately reflect the size of the actual forecast error
Summary
DOA estimation has its origins in the conventional beamforming (CBF) [1], which directly corresponds to the traditional Fourier spectrum estimation method from the time domain signal processing method to the spatial domain signal processing, such that the array angle resolution is restricted by the Rayleigh limit constraint. e Rayleigh limit means that the two signals can be distinguished only when the angular separation between the two far-field signal sources is greater than the antenna beamwidth. e Capon method [2] can minimize the output energy in the interference direction while keeping the output energy in the desired direction constant. is method does not require the number of sources in advance and is robust, but its resolution is not high enough. E Pisarenko method is a harmonic analysis method [3] It obtains the signal subspace and noise subspace by performing eigenvalue decomposition or singular value decomposition on the array covariance matrix and uses the orthogonality between each other. E eigenvector corresponding to the smallest eigenvalue is taken as the noise subspace, and a high-precision DOA estimation of the target is obtained with a small computational cost. This algorithm has limitations because it is only suitable for the number of array elements that exceeds the number of signal sources by one. There is a DOA estimation algorithm based on the compressed sensing theory, which converts the DOA estimation problem into a row sparse matrix reconstruction problem and reduces the amount of calculation by singular value decomposition
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