Low Computational Complexity Joint Angle-Range Estimation Algorithm for FDA-MIMO Radar

Frequency diversity array - multiple input multiple output (FDA-MIMO) radar is a radar system with angle-range two-dimensional dependence. Based on the two-dimensional dependence of FDA-MIMO radar, a tensor-based fast angle-range estimation approach with low computational complexity (CC) is proposed. This algorithm is based on the tensor-based propagation method (PM) algorithm and cleverly utilizes the original structural information of multidimensional data to improve estimation accuracy. Different from traditional tensor-based methods, this algorithm omits the process of high-order singular value decomposition (HOSVD), thus greatly reducing the CC. Specifically, a tensor-based model is considered, and then operator matrices in different directions are obtained by constructing a covariance matrix. Next, the signal subspace is constructed using the operator matrices, and finally, the selection matrices and signal subspace are employed to achieve the angle and range estimation. The proposed algorithm not only ensures estimation accuracy while significantly reducing CC, but is also suitable for massive FDA-MIMO radar systems. The performance advantage and effectiveness of this algorithm can be demonstrated through simulation experiments.