Sparse Auto-Calibration for Radar Coincidence Imaging with Gain-Phase Errors

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.

Waveform Analysis and Optimization for Radar Coincidence Imaging with Modeling Error

“…In recent years, sparse recovery and compressive sensing (CS) have been a hot topic and applied to radar imaging including RCI, by considering the sparse prior of target [2,[14][15][16]. The sparse recovery accuracy is determined by the correlations between the columns of the dictionary matrix [4]; thus minimizing the coherence measure ensures theoretical guarantee for sparse support recovery of signals with potentially higher sparsity level.…”

“…In this paper, we focus on the waveform design (more precisely, FH code design) for FH-RCI with modeling error, since the modeling error generally exists, for example, gainphase error [2,14], off-grid error [15,16,22], and array position error [23]. Modeling error would destroy the ideal assumption that the reference matrix is accurately known; thus the performance of RCI degrades significantly [24,25].…”

Electromagnetic Vortex-Based Radar Imaging Using a Single Receiving Antenna: Theory and Experimental Results

“…As a result, the phase pattern appears as plane waves illuminating from continuous azimuth simultaneously, achieving angular diversity. As radar coincidence imaging uses the stochastic radiation field [16], the helical phase front may provide the OAM-carrying EM waves the ability of azimuth resolution without requiring relative motion [11]. …”

Radar coincidence imaging with phase error using Bayesian hierarchical prior modeling

“…14, a sparse autocalibration imaging (SACI) method is presented to solve the RCI with gainphase error. 3 From the Bayesian statistics perspective, sparse imaging via expectation maximization algorithm and sparse self-calibration method via iterative minimization (SSCIM) algorithm are proposed, respectively, to alleviate the influence of phase synchronization mismatch by exploiting the maximum a posterior (MAP) criterion. 15,16 Likewise, MAP estimator is also used into ISAR imaging by exploiting the sparseness prior of ISAR image, [17][18][19] while the phase error is corrected via modified quasi-Newton algorithm.…”

“…[1][2][3] RCI can obtain focused high-resolution image without the limitation of target relative motion and operate under the observing geometry of forwardlooking/staring, with significant potentials for resolution enhancement, interference, and jamming suppression. In RCI, the temporal-spatial stochastic waveforms are transmitted, thus the spatial variety of wavefront is increased, so the super-resolution within a beam emerges.…”