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In this paper, the two-dimensional (2-D) directionof-arrival (DOA) estimation problem for a mixture of circular and non-circular sources is considered. In particular, we focus on a 2-D array structure consisting of two parallel uniform linear arrays (ULAs) and build a general array model with mixed circular and non-circular sources. The received array data and its conjugate counterparts are combined together to form a new data vector, based on which a series of 2-D DOA estimators are derived. Compared to existing methods, the proposed one has three main advantages. Firstly, it can give a more accurate estimation in situations where the number of sources is within the traditional limit of high resolution methods; secondly, it can still work effectively when the number of mixed signals is larger than that of the array elements; thirdly, the paired 2-D DOAs of the proposed method can be obtained automatically without the complicated 2-D spectrum peak search and therefore has a much lower computational complexity.

In this paper, a two-dimensional (2-D) direction-of-arrival (DOA) estimation method for a mixture of circular and strictly noncircular signals is presented based on a uniform rectangular array (URA). We first formulate a new 2-D array model for such a mixture of signals, and then utilize the observed data coupled with its conjugate counterparts to construct a new data vector and its associated covariance matrix for DOA estimation. By exploiting the second-order non-circularity of incoming signals, a computationally effective ESPRIT-like method is adopted to estimate the 2-D DOAs of mixed sources which are automatically paired by joint diagonalization of two direc

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