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.
Highlights
Radar coincidence imaging (RCI), motivated by classical coincidence imaging in optical systems, is a novel staring imaging technique.[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
We focus on the sparsity-driven RCI with phase error and propose a self-calibration variational message passing (SC-VMP) algorithm in sparse Bayesian learning (SBL) framework
The scattering coefficients are reconstructed by VMP-3L whose performance is shown in Fig. 5, while the performance of phase error estimation is given by Fig. 6, which shows the normalized masea2n0slqouga1r0eðke⌢φrro−r (NMSE) of k2∕kφk2Þ
Summary
Radar coincidence imaging (RCI), motivated by classical coincidence imaging in optical systems, is a novel staring imaging technique.[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, sparse recovery is widely used as the scatterers of target, which are often distributed sparsely in many radar imaging applications. The Cramer–Rao bound for MIMO radar target localization with phase errors has been derived.[5,6] To compensate the phase error, several eigenstructure-based methods are proposed.[7,8,9,10,11] These methods are less sensitive to phase error but lack adaptation to demanding scenarios with low signal-to-noise ratio (SNR), limited snapshots, and spatially adjacent sources.[12] Recently, sparse recovery and compressive sensing[13] are introduced into signal processing by exploiting. We focus on the sparsity-driven RCI with phase error and propose a self-calibration variational message passing (SC-VMP) algorithm in SBL framework. We propose a self-calibration imaging algorithm for joint imaging and phase error calibration in SBL framework.
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