Results 21 to 30 of about 3,432 (185)
Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks
International Journal of Antennas and Propagation, 2022 This work proposes an effective high-resolution multisource direction-of-arrival (DOA) estimation method in impulsive noise scenarios based on convolutional neural networks (CNNs). First of all, the array observation matrix is preprocessed and fed into a
Dong Chen, Young Hoon Joo
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A Modified Rife Algorithm for Off-Grid DOA Estimation Based on Sparse Representations
Sensors, 2015 In this paper we address the problem of off-grid direction of arrival (DOA) estimation based on sparse representations in the situation of multiple measurement vectors (MMV).
Tao Chen +3 more
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A Priori-Based Subarray Selection Algorithm for DOA Estimation
Sensors, 2020 A finer direction-of-arrival (DOA) estimation result needs a large and dense array; it may, however, encounter the mutual coupling effect, which degrades the performance of DOA estimation.
Linghao Zeng +2 more
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IEEE Access, 2021 In order to solve the problem that the gridless DOA estimation algorithms based on generalized finite rate of innovation (FRI) signal reconstruction model are not suitable for two-dimensional DOA estimation using planar array, a separable gridless DOA ...
Kunda Wang, Lin Shi, Tao Chen
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International Journal of Antennas and Propagation, 2017 A method of direction-of-arrival (DOA) estimation using array interpolation is proposed in this paper to increase the number of resolvable sources and improve the DOA estimation performance for coprime array configuration with holes in its virtual array.
Aihua Liu +3 more
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Direction-of-Arrival Estimation Based on Joint Sparsity
Sensors, 2011 We present a DOA estimation algorithm, called Joint-Sparse DOA to address the problem of Direction-of-Arrival (DOA) estimation using sensor arrays. Firstly, DOA estimation is cast as the joint-sparse recovery problem.
Zhitao Huang, Yiyu Zhou, Junhua Wang
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Deep Networks for Direction of Arrival Estimation With Sparse Prior in Low SNR
IEEE Access, 2023 This work introduces direction of arrival (DOA) estimation considering the sparsity prior in the low signal to noise ratio (SNR) using deep learning (DL).
Yanhua Qin
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Direction of arrival estimation of unknown emitter by deep neural networks with array imperfections
IET Radar, Sonar & Navigation, 2023 In array signal processing, high‐resolution direction‐of‐arrival (DOA) estimation by eigendecomposition method requires knowledge of the array covariance matrix and an exact characterisation of the array.
Min Chen, Xingpeng Mao, Libao Liu
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CaSCADE: Compressed Carrier and DOA Estimation [PDF]
IEEE Transactions on Signal Processing, 2017 Spectrum sensing and direction of arrival (DOA) estimation have been thoroughly investigated, both separately and as a joint task. Estimating the support of a set of signals and their DOAs is crucial to many signal processing applications, such as Cognitive Radio (CR).
Shahar Stein +3 more
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International Journal of Antennas and Propagation, 2015 A novel compressive sensing- (CS-) based direction-of-arrival (DOA) estimation algorithm is proposed to solve the performance degradation of the CS-based DOA estimation in the presence of sensing matrix mismatching. Firstly, a DOA sparse sensing model is
Hongtao Li, Chaoyu Wang, Xiaohua Zhu
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