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Joint Angle Estimation and Array Calibration Using Eigenspace in Monostatic MIMO Radar
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DOI：10.1109/ACCESS.2020.2977221.png)
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Abstract
En 中文
In this article, aiming at the presence of the unknown gain-phase uncertainties in monostatic multiple-input multiple-output (MIMO) radar, an eigenspace based algorithm for joint parameter estimation is proposed to achieve angle and array calibration. The initial estimation of the direction of arrival (DOA) can be obtained from the signal subspace achieved by the eigenvalue decomposition (EVD) of covariance matrix, and then an improved multiple signal classification (MUSIC)-based cost function is established for achieving more accurate estimation of DOA, in which a local searching is only required because of the initial estimation value. Eventually, with the help of the estimated DOAs and the noise subspace, the error vector of array gain-phase can be achieved. The proposed method is not only superior to the classical subspace-based algorithms in terms of angle and array gain-phase error estimation, such as MUSIC-like method, especially for closely-spaced targets, but also can be suitable for non-uniform linear arrays (non-ULA). What's more, the computational complexity of the proposed method can be considerably reduced since only a local searching is needed. Multiple simulation experiments are carried out to illustrate the performance of the proposed scheme.
Keywords:
Estimation
MIMO radar
Direction-of-arrival estimation
Sensor arrays
Calibration
Covariance matrices
Multiple-input multiple-output (MIMO) radar
array calibration
gain-phase error
angle estimation
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