Abstract
Ideal array responses are often desirable to a multiple‐input multiple‐output (MIMO) system. Unfortunately, it may not be guaranteed in practice as the mutual coupling (MC) effects always exist. Current works concerning MC in the MIMO system only account for the uniform array geometry scenario. In this paper, we generalize the issue of angle estimation and MC self‐calibration in a bistatic MIMO system in the case of arbitrary sensor geometry. The MC effects corresponding to the transmit array and the receive array are modeled by two MC matrices with several distinct entities. Angle estimation is then recast to a linear constrained quadratic problem. Inspired by the MC transformation property, a multiple signal classification‐ (MUSIC‐) like strategy is proposed, which can estimate the direction‐of‐departure (DOD) and direction‐of‐arrival (DOA) via two individual spectrum searches. Thereafter, the MC coefficients are obtained by exploiting the orthogonality between the signal subspace and the noise subspace. The proposed method is suitable for arbitrary sensor geometry. Detailed analyses with respect to computational complexity, identifiability, and Cramer‐Rao bounds (CRBs) are provided. Simulation results validate the effectiveness of the proposed method.
Highlights
Multiple-input multiple-output (MIMO) is the technique with the most potential for the next-generation array radar system [1,2,3]
The spatial diversity and waveform diversity enables the MIMO system to achieve a virtual aperture, which is much larger than the physical aperture of the radar system
We focus on the bistatic MIMO system, which is a typical representative of the colocated MIMO system
Summary
Multiple-input multiple-output (MIMO) is the technique with the most potential for the next-generation array radar system [1,2,3]. By interpreting the sensors at both ends of the arrays as instrumental sensors, the ESPRIT-like algorithms were introduced in [20, 21], where the MC effect is suppressed by using a selection matrix, and the ESPRIT technique is followed to obtain closeform solutions for DOD and DOA estimation. To the best of our knowledge, only a few efforts have been devoted to robust DOA estimation for arbitrary geometry sensor array with unknown MC. In such a case, the MC matrix exhibits no special structure except Hermitian. The identity matrix is denoted by I, the M × N full one matrix is denoted by 1M×N , the full zero matrix is denoted by 0. ⊗ , ⊙ , and ⊕ represent, respectively, the Kronecker product, the KhatriRao product, and the Hadamard product; diag f·g denotes the diagonalization operation; rank ð·Þ denotes rank operator; and Re ð·Þ and Im ð·Þ return the real part and the image part of a vector, respectively
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