Sparse Bayesian Perspective for Radar Coincidence Imaging With Array Position Error

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Abstract
Radar coincidence imaging (RCI) has shown significant potentials in high-resolution staring imaging without the requirement of target relative motion. To reconstruct the target image, sparsity-driven methods are commonly applied to RCI, while the prior knowledge of imaging model requires to be known accurately. However, inaccuracies in model generally exist, which may defocus the reconstructed image. In this paper, we focus on sparsity-driven RCI with array position error (APE) and propose two sparse auto-calibration imaging algorithms in sparse Bayesian learning framework, i.e., sparse auto-calibration imaging via equivalent phase compensation (SACI-EPC) and sparse auto-calibration imaging via APE compensation (SACI-APEC), respectively. SACI-EPC treats the APE-induced model error as equivalent phase error, and SACI-APEC approximates the imaging model using Taylor expansion. Then Gaussian-Gamma-Gamma sparse prior is assigned to the target, and the model error is determined as part of the imaging process. The two algorithms work by iterating through steps of target reconstruction and model error estimation and compensation. Simulation results show that the proposed algorithms can calibrate the model error and obtain a well-focused target image with high reconstruction accuracy.
Funding Information
  • National Natural Science Foundation of China (61302149, 61571011)

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