An Underdetermined Source Number Estimation Method for Non-Circular Targets Based on Sparse Array

Zhang, Yankui; Wang, Daming; Xu, Haiyun; Mei, Fengtong; You, Jiangdong

doi:10.1109/access.2019.2922382

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…The number of sensors can affect the virtual array aperture and freedom degree, thus having an important influence on RMSE performance. In this simulation, we set SNR = 0dB, snapshots T = 500, Monte Carlo simulations K = 100 and varies the number of sensors to M = [4,8,12,16,20]. Note that each level of nested array has M /2 sensors and there are 3 sources impinging on antenna array.…”

Section: E Rmse Comparison Under Different Number Of Sensorsmentioning

confidence: 99%

“…Hence it can expand the array aperture, increase the degrees of freedom and the accuracy, do which circular signals is not able to. Since most existing algorithms [13], [14] use second order statistics (covariance) to resolve DOAs, we can utilize the non-circular characteristics [15], [16] to obtain much more effective performance. As a result, it always makes sense to concentrate on the non-circular signals and study the impact it brings about in nested array.…”

Section: Introductionmentioning

confidence: 99%

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Underdetermined Direction of Arrival Estimation of Non-Circular Signals via Matrix Completion in Nested Array

et al. 2019

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Direction-of-arrival (DOA) estimation using nested linear array ignores the information from repeated sensors, thus involves the problem of losing accuracy. Moreover, non-circular feature of signals is rarely considered and it results in discontinuity of the virtual array, namely, holes appear. This paper first provide an improved data averaging method to increase the accuracy of DOA estimation by fusing data from covariance and elliptic covariance. And then an algorithm based on matrix completion theory in nested array that greatly extends the degrees of freedom (DOF) and is able to find more sources than number of physical sensors is presented. The algorithm reconstructs the covariance matrix of the virtual linear array, which exactly has the same shift invariance as the uniform linear array but a higher aperture. Considering about the missing elements of the virtual array covariance matrix, we apply matrix completion theory to solve the problem. Finally, true DOAs of multiple signals can be obtained through subspace algorithms. Numerical results demonstrate that the proposed algorithms can obtain high accuracy while underdetermined DOA estimation is realized.INDEX TERMS Direction-of-arrival, nested array, matrix completion theory, data averaging.

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Section: E Rmse Comparison Under Different Number Of Sensorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Underdetermined Direction of Arrival Estimation of Non-Circular Signals via Matrix Completion in Nested Array

et al. 2019

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“…However, because DOA is unstable, the estimation probability of the source number is subject to changes. To achieve underdetermined source number estimation, Zhang Y et al, used the spatial smoothing method to build a virtual array [ 9 ], but this method resulted in a loss of degrees of freedom. Overall, the use of sparse arrays can improve the degrees of freedom of the array, but the methods for source number estimation and DOA estimation are also more complex [ 10 ].…”

Section: Introductionmentioning

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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“…Based on the difference co-array, spatial smoothing MU-SIC (SS-MUSIC) [25] has been carried out which is in fact the reconstruction of elements in covariance matrix of sparse array based on the Toeplitz characteristic of covariance matrix in uniform array as explained in [26] [27]. Covariance matrix interpolation approach (CMIA) has also been carried out with nuclear norm minimization by interpolating additional sensors to the discontinuous different coarray [28]- [33] and make full use of all difference co-array output while SS-MUSIC can only use the continuous part.…”

Section: Introductionmentioning

confidence: 99%

Fast Underdetermined DOA Estimation Based on Generalized MRA via Original Covariance Vector Sparse Reconstruction

Geng¹,

He²,

Yu³

et al. 2021

IEEE Access

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Minimum redundancy array (MRA) has the maximum aperture with continuous difference co-array among various sparse arrays with same number of physical sensors, but it is hard to calculate the sensor position of MRA and realize array design by using MRA. To solve those problem, generalized MRA is proposed with mutual coupling limitation and easy calculation method of sensor position. Based on proposed array configuration, a high-precision underdetermined direction of arrival (DOA) estimation method is proposed with reduced computaional complexity. In this method, fast covariance matrix reconstruction is achieved by trace norm minimization with more accurate covariance estimation. Based on the Toeplitz property of covariance matrix in uniform array, a new sparse representation model is established with reduced dimension of covariance vector and faster DOA estimation is achieved via convex optimization. In addition, the proposed method can also be used for underdetermined DOA estimation of other sparse arrays. Using simulation experiments, we demonstrate that the proposed sparse array configuration has superiority over other sparse arrays and the proposed method can outperform most existing methods in terms of underdetermined DOA estimation accuracy and efficiency.

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An Underdetermined Source Number Estimation Method for Non-Circular Targets Based on Sparse Array

Cited by 6 publications

References 28 publications

Underdetermined Direction of Arrival Estimation of Non-Circular Signals via Matrix Completion in Nested Array

Underdetermined Direction of Arrival Estimation of Non-Circular Signals via Matrix Completion in Nested Array

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

Fast Underdetermined DOA Estimation Based on Generalized MRA via Original Covariance Vector Sparse Reconstruction

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