Efficient 2-D Direction Finding Based on the Real-valued Subspace Linear Transformation with Nonuniform Circular Array
The central symmetry based on the virtual array is a necessary fundamental assumption for the structure transformation of Uniform Circular Arrays(UCAs).In this paper,the virtual signal model for circular arrays is used to make an eigen analysis,and an efficient two-dimensional direction finding algorithm is proposed for arbitrary UCAs and Non Uniform Circular Arrays(NUCAs),where the structure transformation of linear arrays is avoided.As such,the Forward/Backward average of the Array Covariance Matrix(FBACM)and the sum-difference transformation method after separating the real and imaginary parts are both utilized to obtain the manifold and real-valued subspace with matching dimensions.Moreover,the linear relationship between the obtained real-valued subspace and the original complex-valued subspace is revealed,where the spatial spectrum is reconstructed without fake targets.The proposed method can be generalized to NUCAs,enhancing the adaptability of real-valued algorithms to circular array structures.Numerical simulations are applied to demonstrate that with significantly reduced complexity,the proposed method in this paper can provide similar performances and better angle resolution as compared to the traditional UCAs based on the mode-step.Meanwhile,the proposed method demonstrates high robustness with amplitude and phase errors in practical scenarios.
Direction Of Arrival(DOA)Real-valued estimationCircular array