DOA Estimation With Enhanced DOFs by Exploiting Cyclostationarity
Many modulated signals exhibit a cyclostationarity property, which has been applied in direction of arrival estimation due to its immunity to interference and noise. In this paper, we focus on how this cyclostationarity can be effectively integrated with the spatial dimension to enhance both the degrees of freedom and the accuracy of DOA estimation. First, for narrowband signal case, by vectorizing the second-order cyclic correlation matrix (instead of the conventional zero-lag covariance matrix), one can directly generate an augmented virtual array with sensors at positions defined by the difference coarray of the physical array. Then for wideband signal case, two additional fractional factors are introduced so that the vectorized cyclic correlation matrix can be viewed as a single snapshot received signal from an array with sensors at positions defined by the fractional weighted difference coarray. Due to the fact that cyclic correlation matrices, as special second-order statistics, allow us to obtain more degrees of freedom, the proposed two virtual array models can resolve $O(N^2)$ sources by using a sparse linear array consisting of only $N$ physical sensors (e.g., nested/coprime array). Furthermore, a scheme of multipseudosampling is proposed in order to reduce the sensitivity to noise and parameter. The proposed models can be considered as an extension of difference coarray perspective via the combination of cyclic frequency and temporal lag. In the end, numerical simulation results validate the effectiveness of the proposed models.