DOA Estimation Based on Weighted l1-norm Sparse Representation for Low SNR Scenarios

Zuo, Ming; Xie, Shuguo; Zhang, Xian; Yang, Meiling

doi:10.3390/s21134614

Cited by 10 publications

(8 citation statements)

References 20 publications

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“…Regardless of the quantity of sources, they perform better during estimation for sparse arrays when the SNR is higher than 10 dB RMSE of DOA estimation for ULA and sparse arrays for all valid sources (sources numbered from 1 to 18) at the same SNR. Comparing the RMSE of the algorithm proposed in this paper with the differential co-array joint MUSIC algorithm (DCAM) [ 27 ], L1SVD algorithm [ 28 ], and L1CMSR algorithm [ 29 ] for sparse arrays of the same array type, for ULA, the MUSIC algorithm [ 30 ] is used for multi-objective DOA estimation with the same array type. Only consecutive differential joint array elements can be used in the simulation experiments of this paper when using the differential common array algorithm.…”

Section: Simulation Experiments and Analysis Of Resultsmentioning

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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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%

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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“…The comparative recovery performance of the aforementioned algorithms is analyzed in terms of the RMSE of DoA estimates defined as [34,62,63]…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The comparative recovery performance of the aforementioned algorithms is analyzed in terms of the RMSE of DoA estimates defined as [ 34 , 62 , 63 ]

where

is the total number of Monte Carlo trials and

is the estimated result of

in the i th Monte Carlo trial.…”

Section: Simulation Resultsmentioning

confidence: 99%

Joint Model-Order and Robust DoA Estimation for Underwater Sensor Arrays

Hamid

Wyne

Butt

2023

Sensors

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The direction-of-arrival (DoA) estimation algorithms have a fundamental role in target bearing estimation by sensor array systems. Recently, compressive sensing (CS)-based sparse reconstruction techniques have been investigated for DoA estimation due to their superior performance relative to the conventional DoA estimation methods, for a limited number of measurement snapshots. In many underwater deployment scenarios, the acoustic sensor arrays must perform DoA estimation in the presence of several practical problems such as unknown source number, faulty sensors, low values of the received signal-to-noise ratio (SNR), and access to a limited number of measurement snapshots. In the literature, CS-based DoA estimation has been investigated for the individual occurrence of some of these errors but the estimation under joint occurrence of these errors has not been studied. This work investigates the CS-based robust DoA estimation to account for the joint impact of faulty sensors and low SNR conditions experienced by a uniform linear array of underwater acoustic sensors. Most importantly, the proposed CS-based DoA estimation technique does not require a priori knowledge of the source order, which is replaced in the modified stopping criterion of the reconstruction algorithm by taking into account the faulty sensors and the received SNR. Using Monte Carlo techniques, the DoA estimation performance of the proposed method is comprehensively evaluated in relation to other techniques.

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“…by the matrix vec operator and calculate the l 2 -norm. To reduce the calculation, the array signal can be sparsely represented as [22] where…”

Section: Flow Chart Of the Parallel Pi Algorithmmentioning

confidence: 99%

A novel DOA estimation method for an antenna array under strong interference

Zuo

Xie

2022

EURASIP J. Adv. Signal Process.

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Strong interference will affect direction of arrival (DOA) estimation of weak desired signal and even cause DOA estimation failure. This paper investigates the weak signal DOA estimation for an antenna array under strong interference signals, and proposed a novel DOA estimation method for strong interference source suppression and weighted l1-norm sparse representation. A parallel adaptive beamforming algorithm based on power inversion is used to suppress strong interference and form new array data. To reduce spurious peaks in the spectrum under strong interference, a weighted matrix is determined by the optimized subspace algorithm for the subspace projection. Then, the DOA estimation, which is calculated by weighted l1-norm sparse representation, is formed by the weighted matrix and new array data. In this paper, the superiority of the proposed algorithm is illustrated by an example of unmanned aerial vehicle (UAV) video signal DOA estimation under strong interference signals. The simulated results of an orthogonal frequency division multiplexing signal indicate that the proposed algorithm shows merits of fewer snapshots, a sharper main lobe, a lower average noise spectrum value, higher DOA estimation accuracy and success rate. For validation, an outdoor experiment was conducted which demonstrated that the proposed algorithm is superior to other algorithms and can be used for DOA estimation of UAV video signals under strong WiFi interference. Both the simulations and experiments verify that the proposed algorithm can effectively suppress strong interference and achieve better DOA estimation performance for weak signals.

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DOA Estimation Based on Weighted l1-norm Sparse Representation for Low SNR Scenarios

Cited by 10 publications

References 20 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

Joint Model-Order and Robust DoA Estimation for Underwater Sensor Arrays

A novel DOA estimation method for an antenna array under strong interference

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