A Novel Direction-of-Arrival Estimation Algorithm Without Knowing the Source Number

Feng, Chen; Yang, Desen; Wu, Di; Gui, Chenyang; Mo, Shiqi

doi:10.1109/lcomm.2020.3029197

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…Under the above conditions, the RMSEs of the proposed algorithm for DOA estimation of sparse arrays and ULAs for all valid sources (number of sources from 1 to 18) at the same snapshot are calculated. For sparse arrays, the algorithm is compared with the DCAM [ 20 ], L1SVD [ 21 ], and L1CMSR [ 22 ] algorithms for the RMSE of DOA estimation under the same conditions, and with the MUSIC [ 23 ] algorithm for RMSE comparison for ULA. The maximum number of measurable sources for each algorithm is consistent with the above.…”

Section: Simulation Experiments and Analysis Of Resultsmentioning

confidence: 99%

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Research on Underdetermined DOA Estimation Method with Unknown Number of Sources Based on Improved CNN

Zhao

Zhou

et al. 2023

Sensors

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This paper proposes a joint estimation method for source number and DOA based on an improved convolutional neural network for unknown source number and undetermined DOA estimation. By analyzing the signal model, the paper designs a convolutional neural network model based on the existence of a mapping relationship between the covariance matrix and both the source number and DOA estimation. The model, which discards the pooling layer to avoid data loss and introduces the dropout method to improve generalization, takes the signal covariance matrix as input and the two branches of source number estimation and DOA estimation as outputs, and achieves the unfixed number of DOA estimation by filling in invalid values. Simulation experiments and analysis of the results show that the algorithm can effectively achieve the joint estimation of source number and DOA. Under the conditions of high SNR and a large snapshot number, both the proposed algorithm and the traditional algorithm have high estimation accuracy, while under the conditions of low SNR and a small snapshot, the algorithm is better than the traditional algorithm, and under the underdetermined conditions, where the traditional algorithm often fails, the algorithm can still achieve the joint estimation.

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Section: Simulation Experiments and Analysis Of Resultsmentioning

confidence: 99%

“…Therefore, the number of signal sources can be calculated from the number of noise covariance eigenvalues. In fact, there is an error between the covariance matrix calculated from the sampled signal and the true value; the noise covariance eigenvalues are no longer identical [ 20 ], i.e.,

…”

Section: Signal Modelmentioning

confidence: 99%

Research on Underdetermined DOA Estimation Method with Unknown Number of Sources Based on Improved CNN

Zhao

Zhou

et al. 2023

Sensors

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“…A spatial spectrum estimation algorithm is the MUSIC algorithm. The main logic behind the MUSIC algorithm is to separate the signal from noise by Eigen Value Decomposition (EVD) [3]. EVD is applied to the covariance matrix of the received signal.…”

Section: Introductionmentioning

confidence: 99%

Improved Direction of Arrival Estimation using Multiple Signal Classification (MUSIC) Algorithm with Decomposition and Normalization

Jagtap,

Kunte

2023

IJRITCC

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It is important to determine the direction of arrival (DoA) of targets in various applications such as radar and sonar. Multiple Signal Classification (MUSIC), Estimation of Signal Parameters with Rotational In variance Technique (ESPRIT), and Weighted Subspace Fitting (WSF) are subspace-based methods that can be used to improve DoA estimation. MUSIC is effective for high-resolution, uncorrelated signals, but may struggle in cases where there are two nearby targets with a low signal-to-noise ratio (SNR). The goal of this research is to improve the performance of the MUSIC algorithm for DoA estimation with low SNR signals. The proposed solution involves decomposing and normalizing the signal during transmission. Simulations were conducted to test the modified procedure with MUSIC algorithm for DoA estimation, and it was found that received signal power improved though there is noisy environment as well as system can detect more number of targets. The proposed technique of decomposition and normalization could also be applied in other areas such as WiFi communication, autonomous vehicles and biomedical signal and image processing etc.

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“…In [16], a reweighted regularized sparse recovery algorithm has been proposed for the DoA estimation with the unknown mutual coupling of the ULA, assuming the signal number is known in high SNR (around 10dB). In [17], an estimation method based on the spatial spectrum with a fixed eigenvalue order has been proposed to estimate DoA under the circumstance of high SNR (above 0dB), without considering the mutual coupling and the number of sources. For these reasons mentioned, when the number of signals is unknown, the receiving array is inaccurate and the SNR is low, conventional approaches are not so effective and comprehensive, in particular for super-resolution DoA estimation.…”

Section: Introductionmentioning

confidence: 99%

Robust DoA Estimation Using Denoising Autoencoder and Deep Neural Networks

Chen

Shi

et al. 2022

IEEE Access

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As one of the most critical technology in array signal processing, direction of arrival (DoA) estimation has received a great deal of attention in many areas. Traditional methods perform well when the signal-to-noise ratio (SNR) is high and the receiving array is perfect, which are quite different from the situation in some real applications (e.g., the marine communication scenario). To get satisfying performance of DoA estimation when SNR is low and the array is inaccurate (mutual coupling exist), this paper introduces a scheme consisting of denoising autoencoder (DAE) and deep neural networks (DNN), referred to as DAE-DNN scheme. DAE is used to reconstruct a clean ''repaired'' input from its corrupted version to increase the robustness, and then divide the input into multiple parts in different sub-areas. DNN is used to learn the mapping between the received signals and the refined grids of angle in each sub-areas, then the outputs of each sub-areas are concatenated to perform the final DoA estimation. By simulations in different SNR regimes, we study the performance of DAE-DNN in terms of the different snapshots, batch size, learning rate, and epoch. Our results demonstrate that the proposed DAE-DNN scheme outperforms traditional methods in accuracy and robustness.INDEX TERMS DoA, SNR, denoising autoencoder, deep neural networks, mutual coupling.

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A Novel Direction-of-Arrival Estimation Algorithm Without Knowing the Source Number

Cited by 4 publications

References 19 publications

Research on Underdetermined DOA Estimation Method with Unknown Number of Sources Based on Improved CNN

Research on Underdetermined DOA Estimation Method with Unknown Number of Sources Based on Improved CNN

Improved Direction of Arrival Estimation using Multiple Signal Classification (MUSIC) Algorithm with Decomposition and Normalization

Robust DoA Estimation Using Denoising Autoencoder and Deep Neural Networks

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