Abstract
This paper presents a novel Inverse Synthetic Aperture Radar Imaging (ISAR) algorithm based on a new sparse prior, known as the logarithmic Laplacian prior. The newly proposed logarithmic Laplacian prior has a narrower main lobe with higher tail values than the Laplacian prior, which helps to achieve performance improvement on sparse representation. The logarithmic Laplacian prior is used for ISAR imaging within the Bayesian framework to achieve better focused radar image. In the proposed method of ISAR imaging, the phase errors are jointly estimated based on the minimum entropy criterion to accomplish autofocusing. The maximum a posterior (MAP) estimation and the maximum likelihood estimation (MLE) are utilized to estimate the model parameters to avoid manually tuning process. Additionally, the fast Fourier Transform (FFT) and Hadamard product are used to minimize the required computational efficiency. Experimental results based on both simulated and measured data validate that the proposed algorithm outperforms the traditional sparse ISAR imaging algorithms in terms of resolution improvement and noise suppression.
Highlights
Due to the capability of achieving high resolution images of moving targets, the Inverse Aperture Radar Imaging (ISAR) technique has been used for various civil and military applications [1,2,3]
We propose a novel sparse Bayesian ISAR imaging algorithm with a newly proposed logarithmic Laplacian prior, which is achieved by putting a logarithm on the exponent of the Laplacian prior
This section is to derive the Bayesian ISAR imaging based on the logarithmic Laplacian prior
Summary
Due to the capability of achieving high resolution images of moving targets (aircrafts, satellites, vessels, etc.), the Inverse Aperture Radar Imaging (ISAR) technique has been used for various civil and military applications [1,2,3]. Sensors 2016, 16, 611 matching pursuit (OMP) [13], etc These sparse recovery algorithms often suffer from sensitiveness to noise, low computational efficiency or manually tuning of algorithm parameters. The Laplacian prior is utilized in [16,20] to model the ISAR image of the target, and the sparse signal recovery with Laplacian prior is accomplished by the maximum a posterior (MAP) estimation and the quasi-Newton method. The fast Fourier transform (FFT) and Hadamard product are utilized to ensure computational efficiency of the proposed algorithm Both simulated and measured data based experimental results validate the effectiveness of the prosed method.
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