Radar coincidence imaging with phase error using Bayesian hierarchical prior modeling

Abstract

Abstract. Radar coincidence imaging (RCI) is a high-resolution imaging technique without the limitation of relative motion between target and radar. In sparsity-driven RCI, the prior knowledge of imaging model requires to be known accurately. However, the phase error generally exists as a model error, which may cause inaccuracies of the model and defocus the image. The problem is formulated using Bayesian hierarchical prior modeling, and the self-calibration variational message passing (SC-VMP) algorithm is proposed to improve the performance of RCI with phase error. The algorithm determines the phase error as part of the imaging process. The scattering coefficient and phase error are iteratively estimated using VMP and Newton's method, respectively. Simulation results show that the proposed algorithm can estimate the phase error accurately and improve the imaging quality significantly.

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].…”

“…However, only the correlation between y and S cannot generate a high-resolution image for RCI, because the practical microwave waveform cannot generate complete temporalspatial stochastic radiation field due to the limited bandwidth and array aperture. Fortunately, the linear inverse model in (7) can be also solved using optimization algorithms, while ‖y − S • ‖ 2 2 is used as the objective function. An intuitionistic approach is to employ the least square (LS) estimation, which is expressed as…”

“…Certainly, a smaller modeling error ΔS means a minor difference between S and S and will generate a smaller RIE when the condition number of reference matrix is fixed. Unfortunately, even a small system error, for example, gainphase error and array position error, will generate a large modeling error [2,14,23]. To minimize the modeling error, we should decrease the RCI system error or obtain the system parameters accurately.…”

“…Radar coincidence imaging (RCI) is a novel staring imaging technique without the limitation of the target relative motion [1,2]. Originated from the optical coincidence imaging, RCI imitates the randomly fluctuant optical-field by constructing temporal-spatial stochastic radiation field in the imaging area.…”