A Gridless DOA Estimation Method for Sparse SensorArray

Direction-of-arrival (DOA) estimation is still a pivotal research direction in array signal processing. Traditional algorithms based on the signal subspace and compressed sensing theory usually suffer from off-grid and computational complexity. Deep-learning-based methods usually face difficulty in...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 15; no. 22; p. 5281
Main Authors Gao, Sizhe, Ma, Hui, Liu, Hongwei, Yang, Junxiang, Yang, Yang
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2023
Subjects
Algorithms
Analysis
Arrays
Complexity
Datasets
Deep learning
Dictionaries
Direction of arrival
Explosions
Methods
Noise
Sensor arrays
Sensors
Signal processing
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Summary:Direction-of-arrival (DOA) estimation is still a pivotal research direction in array signal processing. Traditional algorithms based on the signal subspace and compressed sensing theory usually suffer from off-grid and computational complexity. Deep-learning-based methods usually face difficulty in obtaining labeled datasets. With the development of array technology, sparse sensor arrays can effectively reduce the number of sensors, which in turn reduces the complexity of the hardware. Therefore, effective DOA estimation algorithms for sparse sensor arrays need to be further investigated. An unsupervised deep learning method is proposed here to address the above issues. A training model was built based on the residual network structure. The DOA estimation was implemented using Vandermonde decomposition. Finally, the experimental findings confirmed the efficacy of the proposed algorithms presented in this article.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15225281