## 1 Introduction  
Direction of arrival (DOA) estimation has been applied to a variety of civil and military fields, such as communications, sonar, air traffic control, electronic reconnaissance, etc. in the past decades [1, 2]. A great many DOA estimation algorithms have been proposed, including beamforming-based methods [3], subspace-based methods [4, 5, 6] and sparsity-based methods [7, 8, 9]. These techniques generally consider the source signals to be non-cooperative signals and mainly utilize spatial properties such as the auto-correlation matrix, without taking into account any prior information. However, abundant prior information on signals of interest may be available to achieve better performance in mobile wireless communications, active radar or sonar, and many other applications; for example, known signal waveforms can be exploited to significantly improve DOA estimation accuracy, eliminate interfering signals and simplify computational complexity [10]. A series of algorithms have been developed in recent decades to deal with the problem of DOA estimation with known waveforms, including decoupled maximum likelihood (DEML) [11], coherent decoupled maximum likelihood (CDEML) [12], white coherent decoupled maximum likelihood (WCDEML) [13], parallel decomposition (PADEC) [14], subarray beamforming-based DOA (SBDOA) [15], linear operators (LP) [16] and more recent research [17, 18, 19, 20, 21, 22, 23]. The previous papers have proven that the Cramer-Rao Bound (CRB) and root-mean-square error (RMSE) of DOA estimation for signals with known waveforms are much lower than those with unknown waveforms [10, 11]. Note that these algorithms mainly focus on using a uniform linear array (ULA) [15], sparse linear array (SLA) [18] or nonuniform linear array [19] to estimate signals' one-dimensional (1D) DOA. However, a linear array only provides 180\({}^{\circ}\) azimuthal coverage, while a planar array is a more attractive configuration known for its ability to provide 360\({}^{\circ}\) azimuthal coverage and elevation angle information in addition. Moreover, when it comes to using a planar array such as uniform circular array (UCA) to estimate the above two-dimensional (2D) DOA, some algorithms specially designed for ULA, such as [6; 14; 15; 16], may become invalid or need some complicated transformations [24]. On the other hand, the existing DOA estimators with known waveforms are often involved in eigenvalue decomposition [14], finding polynomial roots [13] or spectrum peak search [22], the computational complexity of which is too burdensome to realize in practice. In fact, interferometer-based methods are more suitable and widely used for DOA estimation with advantages of higher efficiency, broader flexibility and easier realization [25; 26; 27; 28; 29]. Up to now, there has been only one study concerning an interferometer-based DOA estimator with a known waveform [30]; however, it was only designed for one source using ULA. In view of the above two aspects, we take advantage of the interferometer method to solve the 2D DOA estimation problem considering multiple sources with known waveforms using UCA. Extension to other planar arrays, such as uniform rectangular array, L-shape array, etc., is similar and straightforward. The main idea of our method is to exploit auto-correlation and cross-correlation matrix of known waveforms and array output vectors to estimate spatial signature matrix at first. Due to the fact that each column of spatial signature matrix is colinear with steering vector of one source, each 2D DOA is estimated through an interferometer procedure separately and in parallel. The proposed method is computationally efficient and easy to implement in practice, as all 2D DOA estimates are obtained in closed form from a series of least-squared (LS) solutions without searching the parameter space. Furthermore, we also derive the corresponding CRB of 2D DOA with known waveforms to prove the effectiveness of our method.