Fast real-valued tensor decomposition framework for parameter estimation in FDA-MIMO radar

Frequency diversity array -Multiple input multiple output (FDA-MIMO) radar has a two-dimensional angle -range dependence due to the existence of certain frequency offset between transmitting elements. For obtaining angle-range estimation, a fast real-valued tensor propagator method (PM) for FDA-MIMO radar is developed. The developed approach is based on the real-valued tensor PM, which not only utilizes the original structural information of multidimensional information to improve estimation accuracy, but also eliminates the process of high-order singular value decomposition (HOSVD) on multidimensional data, greatly reducing computational complexity. Firstly, the unitary transformation technique is employed to convert the constructed tensor into a real-valued tensor. Next, construct a covariance tensor to obtain operator matrices in different directions. Then, a signal subspace is constructed using operator matrices. Finally, the selection matrices and the obtained signal subspace are employed to estimate angle and range information. The proposed algorithm can not only achieve parameter estimation at low snapshots, but also has much lower computational complexity than other algorithms at high snapshots. Therefore, the developed approach greatly reduces computational complexity while ensuring estimation accuracy, which enables it to be applied to massive FDA-MIMO radars. Simulation results confirm the accuracy advantage and high-efficiency of our algorithm.

Automated summary

This article presents a fast real-valued tensor propagator method for FDA-MIMO radar. The method improves estimation accuracy by utilizing the original structural information of multidimensional data and eliminates the high computational complexity of high-order singular value decomposition. The proposed algorithm achieves parameter estimation at low snapshots and has lower computational complexity than other algorithms at high snapshots.