Coherent compensation and high‐resolution technology of multi‐band inverse synthetic aperture radar fusion imaging

Abstract

Abstract Multi‐band inverse synthetic aperture radar (ISAR) fusion imaging technology can effectively improve the range resolution without incurring high hardware cost. The coherent phase between sub‐bands is a prerequisite to achieve multi‐band ISAR fusion imaging. Here, a joint approach of coherent compensation and high‐resolution imaging is proposed to compensate the incoherent phase and obtain high‐resolution ISAR fusion images. First, an incoherent phase estimation model based on sparse representation is established, and the phase estimation accuracy is improved by a modified coherent dictionary in case of off‐grid. Then, a multi‐band ISAR fusion imaging model based on sparse representation is established. The complex Gaussian scale mixture priors and the complex Gaussian priors are imposed on the scatterers and noise, respectively. The solution is derived in the complex domain based on the variational Bayesian expectation maximization framework. The proposed method can not only achieve better incoherent phase compensation in the case of off‐grid, but also obtain high‐quality ISAR fusion images under low signal‐to‐noise ratio and low bandwidth sampling ratio. Experimental results verify the effectiveness and robustness of the proposed method based on both numerical simulations and real data.

Keywords

• expectation‐maximisation algorithm
• radar imaging
• phase estimation
• synthetic aperture radar
• radar resolution
• image fusion