High-resolution multiple-input-multiple-output-inverse synthetic aperture radar imaging based on sparse representation

In this study, a new method based on sparse representation is proposed for multiple-input-multiple-output (MIMO) radar imaging combining with inverse synthetic aperture radar (ISAR) technique. After matched filtering and envelope alignment, the MIMO-ISAR cross-range echo is formulated as a two-dimensional sparse signal with respect to the spatial and slow-time domain. A joint self-calibration and sparse reconstruction algorithm is developed to correct the phase error induced by the target's translational motion and reconstruct the spatial-Doppler scene of the target. Then, the linear relationship between the spatial and Doppler frequencies is utilised to solve the ambiguity of the spatial frequency. Finally, the unambiguous spatial frequency is translated into the cross-range coordinate of the scatterer. Experimental results show that the proposed method is capable of precisely correcting the phase error and achieving high-resolution MIMO-ISAR imaging. In addition, the relative rotation angle required for MIMO-ISAR imaging can also be reduced significantly.