Abstract
Ideal transmitting and receiving (Tx/Rx) array response is always desirable in multiple-input multiple-output (MIMO) radar. In practice, nevertheless, Tx/Rx arrays may be susceptible to unknown gain-phase errors (GPE) and yield seriously decreased positioning accuracy. This paper focuses on the direction-of-departure (DOD) and direction-of-arrival (DOA) problem in bistatic MIMO radar with unknown gain-phase errors (GPE). A novel parallel factor (PARAFAC) estimator is proposed. The factor matrices containing DOD and DOA are firstly obtained via PARAFAC decomposition. One DOD-DOA pair estimation is then accomplished from the spectrum searching. Thereafter, the remainder DOD and DOA are achieved by the least squares technique with the previous estimated angle pair. The proposed estimator is analyzed in detail. It only requires one instrumental Tx/Rx sensor, and it outperforms the state-of-the-art algorithms. Numerical simulations verify the theoretical advantages.
Highlights
Q = 200 Monte-Carlo simulation experiments are carried out to show the improvement of our estimator
We have stressed the issue of joint angle and gain-phase errors (GPE) self-calibration in bistatic multiple-input multiple-output (MIMO)
An improved parallel factor (PARAFAC) estimator was proposed, the root mean square (RMSE) of which is an order of magni
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The problem of DOD, DOA, and GPE vectors estimation is linked to a constrained optimization issue, and it is solved by Lagrange multiplier approach It offers closed-form solutions to DOA and DOD estimation, so it is much more efficient than MUSIC-like. To estimate the angles and GPE vectors from the factor matrices, the Lagrange multiplier method was followed in [15], while the element-wise division operation was chosen in [16] Another PARAFAC estimator was introduced in [17], which first estimates the gain error via element-wise division, and it obtains the angle estimation via the least squares fitting. The proposed estimator is suitable for MIMO radar with only one instrumental Tx/Rx sensor This improvement benefits from the fact that the stochastic feature of the phase error is taken into account in the proposed estimator. Numerical simulations are designed to verify the improvement of the proposed algorithm
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