Direction of Arrival Estimation with Uniform Linear Array based on Recurrent Neural Network

Wajid, Mohd; Kumar, Bipin; Goel, Arun; Kumar, Arun; Bahl, Rajendar

doi:10.1109/ispcc48220.2019.8988399

Cited by 16 publications

(8 citation statements)

References 17 publications

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“…Besides using RNNs for applications related to natural language processing such as speech recognition, RNN networks are also used for DOA estimation [28][29][30]. In [30], the RNN is created based on bidirectional long-short term memory (BiLSTM). RNNs do not directly estimate DOA but classify them based on classes.…”

Section: Related Workmentioning

confidence: 99%

Direction of Arrival Estimation for Coherent Signals’ Method Based on LSTM Neural Network

Thanh

Duc

Van

et al. 2022

Applied Computational Intelligence and Soft Computing

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Radio direction finding system is a system that determines the direction or coordinates of radio signal sources. The main function of this system is to determine the direction of arrival (DOA) of an incident radio wave. DOA information plays an important role in array signal processing and has many applications in communications, radar, seismic survey, etc. In this study, we propose a method to estimate the DOA by using the simulated signal dataset obtained at the linear antenna array (ULA) and the suitable Long Short-Term Memory (LSTM) network model. The performance of the method is evaluated based on the root mean square error (RMSE) parameter and then is compared with 2 other algorithms, multiple signal classification (MUSIC) and deep neural network (DNN) in different cases such as deviation of incoming signals, variation of signal-to-noise ratio (SNR), and coherent incoming signals. The obtained results have shown that the proposed method has significantly improved accuracy compared to other methods.

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Section: Related Workmentioning

confidence: 99%

Direction of Arrival Estimation for Coherent Signals’ Method Based on LSTM Neural Network

Thanh

Duc

Van

et al. 2022

Applied Computational Intelligence and Soft Computing

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“…Fu et al [53] proposed a new blind DOA estimation method that uses the 2D convolution nonnegative matrix factorization method to generate a new array signal to estimate the azimuth angle of the reverberation signal. Wajid et al [55] proposed to use the recurrent neural network (RNN) model to learn some similar features used in DAS beamforming. e results show that the DOA estimation result based on RNN is better than DAS beamforming.…”

Section: Speech Doa Estimationmentioning

confidence: 99%

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Li²,

Rum

2021

Mobile Information Systems

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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.

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“…In most of the literature, two famous array structures are implemented for DOA estimation [5][6][7]. One of them is uniform linear arrays [8][9][10] and the second one is sparse arrays [11,12]. A lot of research has been done on uniform linear arrays (ULA).…”

Section: Introductionmentioning

confidence: 99%

Far-Field DOA Estimation of Uncorrelated RADAR Signals through Coprime Arrays in Low SNR Regime by Implementing Cuckoo Search Algorithm

et al. 2022

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For the purpose of attaining a high degree of freedom (DOF) for the direction of arrival (DOA) estimations in radar technology, coprime sensor arrays (CSAs) are evaluated in this paper. In addition, the global and local minima of extremely non-linear functions are investigated, aiming to improve DOF. The optimization features of the cuckoo search (CS) algorithm are utilized for DOA estimation of far-field sources in a low signal-to-noise ratio (SNR) environment. The analytical approach of the proposed CSAs, CS and global and local minima in terms of cumulative distribution function (CDF), fitness function and SNR for DOA accuracy are presented. The parameters like root mean square error (RMSE) for frequency distribution, RMSE variability analysis, estimation accuracy, RMSE for CDF, robustness against snapshots and noise and RMSE for Monte Carlo simulation runs are explored for proposed model performance estimation. In conclusion, the proposed DOA estimation in radar technology through CS and CSA achievements are contrasted with existing tools such as particle swarm optimization (PSO).

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Direction of Arrival Estimation with Uniform Linear Array based on Recurrent Neural Network

Cited by 16 publications

References 17 publications

Direction of Arrival Estimation for Coherent Signals’ Method Based on LSTM Neural Network

Direction of Arrival Estimation for Coherent Signals’ Method Based on LSTM Neural Network

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Far-Field DOA Estimation of Uncorrelated RADAR Signals through Coprime Arrays in Low SNR Regime by Implementing Cuckoo Search Algorithm

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