A Fast PARAFAC Algorithm for Parameter Estimation in Monostatic FDA-MIMO Radar

Abstract

This paper studies the joint range and angle estimation of monostatic frequency diverse array multiple-input multiple-output (FDA-MIMO) radar and proposes a joint estimation algorithm. First, the transmit direction matrix is converted into real values by unitary transformation, and the Vandermonde-like matrix structure is used to construct an augmented output that doubles the aperture of the receive array. Then the augmented output is combined into a third-order tensor. Next, the factor matrices are initially estimated. Finally, the direction matrices are estimated utilizing parallel factor (PARAFAC) decomposition, and the range and angle are calculated by employing least square fitting. As contrasted with the classic PARAFAC method, the proposed method can estimate more targets and provide better estimation performance, and requires less computational complexity. The availability and excellence of the proposed method are reflected by numerical simulations and complexity analysis.