Abstract
In this paper, a reweighted sparse representation algorithm based on noncircular sources is proposed, and the problem of the direction of arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar with mutual coupling is addressed. Making full use of the special structure of banded symmetric Toeplitz mutual coupling matrices (MCM), the proposed algorithm firstly eliminates the effect of mutual coupling by linear transformation. Then, a reduced dimensional transformation is exploited to reduce the computational complexity of the proposed algorithm. Furthermore, by utilizing the noncircular feature of signals, the new extended received data matrix is formulated to enlarge the array aperture. Finally, based on the new received data, a reweighted matrix is constructed, and the proposed method further designs the joint reweighted sparse representation scheme to achieve the DOA estimation by solving the l 1 -norm constraint minimization problem. The proposed method enlarges the array aperture due to the application of signal noncircularity, and in the presence of mutual coupling, the proposed algorithm provides higher resolution and better angle estimation performance than ESPRIT-like, l 1 -SVD and l 1 -SRDML (sparse representation deterministic maximum likelihood) algorithms. Numerical experiment results verify the effectiveness and advantages of the proposed method.
Highlights
With orthogonal transmitted waveforms, multiple-input multiple-output (MIMO) radar has drawn increasing attention in the field of wireless communications
Utilizing noncircular sources and the sparse representation framework, we aim to eliminate the effect of mutual coupling and achieve a better
Where Q is the total number of the Monte Carlo trials, which is Q = 200 in the simulations
Summary
Multiple-input multiple-output (MIMO) radar has drawn increasing attention in the field of wireless communications. Compared with the conventional phased-array radar, MIMO radar owns a number of advantages, such as higher resolution and better parameter identifiability [1]. MIMO radar can be classified into the following two types: statistical MIMO radar and colocated MIMO radar. Colocated MIMO radar can achieve more degrees of freedom and higher spatial resolution, because its closely-spaced antennas form a virtual array with a large aperture. Colocated MIMO radar includes the monostatic one and the bistatic one.
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