Abstract
Radar coincidence imaging (RCI) is a high-resolution staring imaging technique without the limitation of relative motion between target and radar. The sparsity-driven approaches are commonly used in RCI, while the prior knowledge of imaging models needs to be known accurately. However, as one of the major model errors, the gain-phase error exists generally, and may cause inaccuracies of the model and defocus the image. In the present report, the sparse auto-calibration method is proposed to compensate the gain-phase error in RCI. The method can determine the gain-phase error as part of the imaging process. It uses an iterative algorithm, which cycles through steps of target reconstruction and gain-phase error estimation, where orthogonal matching pursuit (OMP) and Newton’s method are used, respectively. Simulation results show that the proposed method can improve the imaging quality significantly and estimate the gain-phase error accurately.
Highlights
Radar coincidence imaging (RCI), originated from the classical coincidence imaging in optical systems, is a novel staring imaging technique [1,2,3]
We focus on the gain-phase error calibration in sparsity-driven RCI
While compared with orthogonal matching pursuit (OMP), the proposed method improves the imaging performance by more than 8 dB from the relative imaging error (RIE) perspective
Summary
Radar coincidence imaging (RCI), originated from the classical coincidence imaging in optical systems, is a novel staring imaging technique [1,2,3]. Algorithms for joint angles and array gain-phase error estimation in bistatic multiple-input multiple-output (MIMO) radar based on reduced-dimension multiple signal classification (MUSIC) and based on trilinear decomposition are proposed in [6,7,8]. In [11], two new estimation algorithms are proposed to estimate the gain and phase errors, i.e., estimation algorithm for the conventional data model (EACDM) and estimation algorithm for the improved data model (EAIDM) These methods are less sensitive to phase error [12] but lack adaptation to demanding scenarios with low signal-to-noise ratio (SNR), limited snapshots and spatially adjacent sources, just as their counterparts do in accurately calibrated arrays. DOA estimation, and a sparse Bayesian array calibration (SBAC) method is proposed in [12].
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