Nonlinear Frequency Offset Enhanced MNM Parameter Estimation Algorithm for FDA-MIMO Radar

Most of the current research on the frequency diversity array (FDA) multiple-input multiple-output (MIMO) radar parameter estimation problem has focused on the linear frequency offset regime. In low signal-to-noise conditions, MUSIC and ESPRIT algorithms and improved algorithms still suffer from some errors in the estimation of angular and distance parameters for multiple targets. In this paper, a non-linear frequency offset enhanced MNM algorithm based on FDA-MIMO radar is proposed for the joint estimation of angle and distance of multiple targets. Based on the MNM algorithm, the projections on the corresponding eigenvectors are enhanced by weighting the principal eigenvalues in the signal subspace, resulting in an enhanced spatial spectrum of the targets, improved multi-target discrimination, and the estimation of multi-target angle and distance parameters at low signal-to-noise ratios. The numerical simulation results show that the proposed algorithm has excellent reliability and stability.