Robust DOA Estimation Method for MIMO Radar via Deep Neural Networks

Cong, Jingyu; Wang, Xianpeng; Huang, Mengxing; Wan, Liangtian

doi:10.1109/jsen.2020.3046291

Cited by 70 publications

(30 citation statements)

References 22 publications

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“…the OMP-based channel estimation [44], the iterative reweight (IR)-based superresolution channel estimation scheme [16], the Oracle estimator in [44] and the LS estimator. The normalized mean square error (NMSE) which is denoted as E H −Ĥ [45,46]. Fig.…”

Section: Resultsmentioning

confidence: 99%

GAMP-SBL-based channel estimation for millimeter-wave MIMO systems

Shao

Wang

Lan

et al. 2021

EURASIP J. Adv. Signal Process.

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Based on the finite scattering characters of the millimeter-wave multiple-input multiple-output (MIMO) channel, the mmWave channel estimation problem can be considered as a sparse signal recovery problem. However, most traditional channel estimation methods depend on grid search, which may lead to considerable precision loss. To improve the channel estimation accuracy, we propose a high-precision two-stage millimeter-wave MIMO system channel estimation algorithm. Since the traditional expectation–maximization-based sparse Bayesian learning algorithm can be applied to handle this problem, it spends lots of time to calculate the E-step which needs to compute the inversion of a high-dimensional matrix. To avoid the high computation of matrix inversion, we combine damp generalized approximate message passing with the E-step in SBL. We then improve a refined algorithm to handle the dictionary matrix mismatching problem in sparse representation. Numerical simulations show that the estimation time of the proposed algorithm is greatly reduced compared with the traditional SBL algorithm and better estimation performance is obtained at the same time.

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Section: Resultsmentioning

confidence: 99%

GAMP-SBL-based channel estimation for millimeter-wave MIMO systems

Shao

Wang

Lan

et al. 2021

EURASIP J. Adv. Signal Process.

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“…As shown in Table 7, if the number of snapshots in [45] increases from 20 to 100, the MAE drops from 1.6 to 0.1. Increasing the number of snapshots from 50 to 400 in [46] reduces the RMSE from 0.4 to 0.1. Increasing the number of snapshots from 10 to 1,000 in [50] increases the accuracy from 91% to 100%.…”

Section: E Impact Of the Number Of Snapshots On Doa Estimationmentioning

confidence: 99%

“…sional (2D) DOA estimation. Cong et al[46] proposed a DNN-based DOA estimation framework that includes an autoencoder, a feedforward network, a network parameter database, and a collection of a series of directed acyclic graph networks (DAGN). Among them, the autoencoder is equivalent to the noise filter, and each subnet of DAGN is composed of a convolutional neural network (CNN) and two bidirectional long short-term memory (BiLSTM) networks.…”

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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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“…The locationing effect was evaluated using the root mean square error (RMSE) metric. Differently from the single parameter estimation in [24,25], for the multi-parameter estimation problem, we defined the RMSE of the DOA and the RMSE of the range as follows: 6 and 7, the RMSE of the proposed algorithm is better than that of 2D-MUSIC with a low-density grid but worse than that of 2D-MUSIC with a high-density grid. Due to the existence of grid errors, the estimation error of 2D-MUSIC with grids of [1 • , 1 m] cannot be reduced beyond a certain value through the increase of SNR.…”

Section: Simulationsmentioning

confidence: 99%

Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

et al. 2021

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The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.

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Robust DOA Estimation Method for MIMO Radar via Deep Neural Networks

Cited by 70 publications

References 22 publications

GAMP-SBL-based channel estimation for millimeter-wave MIMO systems

GAMP-SBL-based channel estimation for millimeter-wave MIMO systems

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

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