Signal Parameter Estimation Algorithm for Orthogonal Dipole Array Based on Finite Rate of Innovation

CHEN Tao; ZHAO Lipeng; SHI Lin; SHEN Mengyu

doi:10.11999/JEIT210357

Volume 44 Issue 7

Jul. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(7): 2469-2477

CHEN Tao, ZHAO Lipeng, SHI Lin, SHEN Mengyu. Signal Parameter Estimation Algorithm for Orthogonal Dipole Array Based on Finite Rate of Innovation[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2469-2477. doi: 10.11999/JEIT210357

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CHEN Tao, ZHAO Lipeng, SHI Lin, SHEN Mengyu. Signal Parameter Estimation Algorithm for Orthogonal Dipole Array Based on Finite Rate of Innovation[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2469-2477. doi: 10.11999/JEIT210357

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Signal Parameter Estimation Algorithm for Orthogonal Dipole Array Based on Finite Rate of Innovation

doi: 10.11999/JEIT210357

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China

Funds: The National Natural Science Foundation of China (62071137), The National Defense Science and Technology Foundation Strengthening Program (2019-JCJQ-ZD-067-00)

Received Date: 2021-04-25
Rev Recd Date: 2021-08-24

Available Online: 2021-09-15

Publish Date: 2022-07-25

Abstract

Abstract

To deal with the grid mismatch problem of compressed sensing algorithms in polarization-sensitive array Direction Of Arrival (DOA) estimation, a gridless signal parameter estimation algorithm for orthogonal dipole array based on Finite Rate of Innovation (FRI) is proposed. First, two sub-arrays of the uniform orthogonal dipole linear array with the different antenna polarization direction, are used to obtain the sum of their self-correlation covariance matrix, and the covariance matrix satisfying the Toeplitz structure is recovered through the covariance fitting criteria. Then, the covariance matrix is used to construct the FRI signal reconstruction model, and the zeros of the polynomial with the reconstruction result as the coefficient is solved to obtain the estimation result of the DOA parameter of the incident signal. Finally, using the estimated DOA parameters and the self-correlation covariance matrix and cross-correlation covariance matrix of the two sub-arrays, the least square method is used to calculate the polarization parameter estimation results of the incident signal. Simulation experiments show that this algorithm has higher estimation accuracy and angle resolution compared with subspace and compressed sensing algorithms.

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References(22)

References

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