Super-Resolution ISAR Imaging Using the Off-the-Grid Structured Low-Rank Method

Inverse synthetic aperture radar (ISAR) imaging relies on wideband waveform and viewing angle variation to achieve range and cross-range resolutions, respectively. To enhance the resolutions of 2-D images, sparse signal-processing techniques, such as compressed sensing (CS), have been applied to ISAR imaging using a sparse prior. Despite its efficiency in super-resolution imaging, the performance of CS is constrained due to the mismatch of the discrete dictionary, such as the Fourier transform. To address this issue, we propose a novel off-the-grid super-resolution ISAR imaging algorithm that employs a structured low-rank approach to effectively extrapolate the data bandwidth and aperture. To fully capture the low-rank property of ISAR data, the structured data model is constructed and its low-rank property is deduced to exhibit that the signal is embedded in a limited dimensional subspace. Then, the annihilating filter is derived by constructing a structured data matrix to formulate the proposed structured low-rank method, termed as off-the-grid super-resolution using annihilation constraint (OSAC). Taking into account that super-resolution imaging is highly reliant on the accuracy of the annihilating filter, the optimal annihilating filter is also estimated with the updating of extrapolated ISAR data. Through iterative updates of the annihilating filter and solution of the minimization problem, super-resolution ISAR imaging can be achieved by avoiding the discrete mismatch of the conventional CS method. Due to the effective exploration of structured low-rank property, the proposed OSAC algorithm offers superior precision in scatterer location and structure interpretation of a target. Experimental results using both simulated and real data are presented to verify the enhanced performance of 2-D resolution in ISAR imaging.