Direction of arrival (DOA) estimation problem for multiple-input multiple-output (MIMO) radar with unknown mutual coupling is studied, and an algorithm for the DOA estimation based on root multiple signal classification (MUSIC) is proposed. Firstly, according to the Toeplitz structure of the mutual coupling matrix, output data of some specified sensors are selected to eliminate the influence of the mutual coupling. Then the reduced-dimension transformation is applied to make the computation burden lower as well as obtain a Vandermonde structure of the direction matrix. Finally, Root-MUSIC can be adopted for the angle estimation. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm and MUSIC-like algorithm. Furthermore, the proposed algorithm has lower complexity than them. The simulation results verify the effectiveness of the algorithm, and the theoretical estimation error of the algorithm is also derived. 1. Introduction Multiple-input multiple-output (MIMO) radars have attracted a lot of attention recently for their potential advantages over conventional phased-array radars [1–4]. MIMO radar systems can overcome fading effect, enhance spatial resolution, strengthen parameter identifiability, and improve target detection performance for the additional degrees of freedom [5–7]. Angle estimation is a key issue in MIMO radar and has been studied by a lot of researchers. Estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithms [8–10] can obtain the closed-form estimations via rotational invariance in the subspace. Capon algorithm [11] and multiple signal classification (MUSIC) algorithm [12] both estimate the angles via the peak searches. Polynomial root finding technique can transform the peak searches into root finding problem when the arrays are uniform linear arrays (ULA) [13]. Based on the uniqueness of trilinear decomposition, parallel factor analysis (PARAFAC) algorithms [14–16] can also be adopted for the angle estimation. For monostatic MIMO radar, low-complexity ESPRIT [17] and Capon [18] can achieve better angle estimation performance with lighter computation burden based on the reduced-dimension transformation. However, these methods strongly depend on the array manifold, which is often perturbed by the mutual coupling in practical situations. The mutual coupling will make the above methods have performance degradation or even fail to work, and various methods for mutual coupling compensation
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