High-Resolution Radar Imaging of Off-Grid Maneuvering Targets Based on Parametric Sparse Bayesian Learning

Abstract

In high-resolution radar imaging, the time-varying Doppler induced by maneuvering targets generally invalidates traditional methods. Although sparse signal reconstruction methods can be applied to achieve better performance, the off-grid problem embedded in these methods still prohibits well-focused imaging. In this article, we propose to perform high-resolution radar imaging of maneuvering targets with off-grid scattering centers in a Bayesian framework. First, the statistical model with a parametric dictionary is established, in which the Doppler frequencies of scattering centers are treated as unknown model parameters instead of being discretized into the grid. To be consistent with the physical characteristics, the posterior of the Doppler is approximated by the von Mises distribution. Then, the model parameters and rotation parameters are estimated iteratively by variational inference (VI) and the Newton method. Experiments have demonstrated that the proposed method provides an effective way for high-resolution and well-focused radar imaging of maneuvering targets with off-grid scattering centers in complex scenarios, such as incomplete data and low signal-to-noise ratio.