Joint Two-dimensional Direction of Arrival Estimation Based on Cross Covariance Matrix of Parallel Nested Array

LI Jianfeng; JIANG Defu; SHEN Mingwei

doi:10.11999/JEIT160488

Volume 39 Issue 3

Mar. 2017

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LI Jianfeng, JIANG Defu, SHEN Mingwei . Joint Two-dimensional Direction of Arrival Estimation Based on Cross Covariance Matrix of Parallel Nested Array[J]. Journal of Electronics & Information Technology, 2017, 39(3): 670-676. doi: 10.11999/JEIT160488

Citation:

LI Jianfeng, JIANG Defu, SHEN Mingwei . Joint Two-dimensional Direction of Arrival Estimation Based on Cross Covariance Matrix of Parallel Nested Array[J]. Journal of Electronics & Information Technology, 2017, 39(3): 670-676. doi: 10.11999/JEIT160488

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Joint Two-dimensional Direction of Arrival Estimation Based on Cross Covariance Matrix of Parallel Nested Array

doi: 10.11999/JEIT160488

Funds:

The Fundamental Research Funds for the Central Universities (2015B12614), A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions

Received Date: 2016-05-12
Rev Recd Date: 2016-09-06
Publish Date: 2017-03-19

Abstract

Abstract

A Cross Covariance Matrix (CCM) based Two Dimensional (2D) Direction Of Arrival (DOA) estimation algorithm for parallel nested array is proposed. A long virtual array can be achieved based on the CCM between the two parallel nested arrays, and 2D DOA estimation can be transformed to a 1D DOA estimation problem. Thereafter, virtual snapshots are increased by exploiting the Vandermonde structure of direction matrix, and the aperture loss is minimized when constructing covariance matrix from the virtual array. Finally, the proposed algorithm employs unitary Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) and Total Least Squares (TLS) to reduce further the influence of noise and achieve automatically paired 2D DOA estimation. Compared to DOA estimation algorithms using conventional parallel array, the proposed algorithm can achieve better DOA estimation performance, identify more signals and is more robust to spatial color noise. The simulation results verify the effectiveness of the proposed algorithm.
- Two dimensional DOA estimation,
- Unitary ESPRIT,
- Parallel nested array,
- Cross covariance matrix

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References(28)

References

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