The MUSIC (Multiple Signal Classification) technique with the circular array can estimate both elevational and azimuthal direction-of-arrival (DOA). This conventional method can not distinguish coherent signals, therefore, it can not estimate proper DOA in the presence of coherent signals. On the other hand, limited as to uniformly spaced linear arrays, the spatial smoothing technique is shown to be effective approach in decorrelating coherent signals. This scheme can not be applied directly to the nonlinear arrays. To overcome the coherent signal nonseparation problem in the nonlinear arrays, the approach using a linear interpolation technique has been proposed. However, this approach provides DOA estimates in one dimensional. In our proposed method, we use not only a linear interpolation technique for the circular array but also the symmetry of the circular array. The computer simulation is performed to demonstrate the usefulness of our method. As its result shows, the method can perform well even in the presence of coherent signals.

This paper presents two types of two-dimensional (2-D) adaptive beamforming algorithm which have high rate of convergence. One is a linearly constrained minimum variance (LCMV) beamforming algorithm which minimizes the average output power of a beamformer, and the other is a generalized sidelobe canceler (GSC) algorithm which generalizes the notion of a linear constraint by using the multiple linear constraints. In both algorithms, we apply a 2-D lattice filter to an adaptive filtering since the 2-D lattice filter provides excellent properties compared to a transversal filter. In order to evaluate the validity of the algorithm, we perform computer simulations. The experimental results show that the algorithm can reject interference signals while maintaining the direction of desired signal, and can improve convergent performance.