An improved signal-selective direction finding algorithm using second-order cyclic statistics

A new signal-selective direction finding algorithm which exploits the property of the cyclostationarity of incoming signals is proposed. After dimensionality reducing by projecting the observed array data onto the signal subspace, the array manifold matrix is identified by the simultaneous diagonalization structure of the matrix pencil consisting of the cyclic correlation matrix and the cyclic conjugate correlation matrix. Then the direction-of-arrivals (DOAs) are obtained from the phase-differences of the estimated array manifold matrix. Simulation results demonstrate that the proposed algorithm is superior to the cyclic-MUSIC and cyclic-ESPRIT in terms of the root mean squares errors (RMSEs) of the DOA estimates.