Super-Resolution 2D Imaging for MIMO Radar with Unknown Gain and Phase Errors using Generalized ESPRIT Algorithm

Gain and phase errors of the transmitter and receiver arrays will impact the amplitude and phase of the received echo signal, so that the multiple-input multiple-output (MIMO) radar system cannot obtain high-quality target images with theoretical super-resolution in practical applications. To solve this problem, a generalized Estimation of Signal Parameters using Rotational Invariance Technique (GESPRIT) imaging algorithm based on the spatial spectrum estimation theory is proposed for twodimensional (2D) super-resolution target imaging in this paper, and the azimuth statistical resolution limit (SRL) of the MIMO radar is derived. Firstly, a 2D echo signal model of MIMO radar with gain and phase errors is constructed by using geometric approximation and phase centre approximation (PCA). Then, nonlinear least square (NLS) optimization is used to reconstruct the rotation invariant properties of the transmitter and receiver arrays destroyed by gain and phase errors, and the 2D position of each target scattering center is obtained via the ESPRIT algorithm. Finally, a hypothesis test approach based on the approximate generalized likelihood ratio test (AGLRT) is proposed to resolve two closely-separated scattering centers using the first order approximation model. Based on this, the azimuth resolution performance of MIMO radar is derived and analyzed, the closed solution of resolution probability and azimuth SRL is obtained. Simulation results demonstrate the effectiveness of the proposed algorithm and the correctness of the derived SRL.