基于Beta过程的高分辨ISAR成像

doi:10.12305/j.issn.1001-506X.2022.06.13

Abstract

Abstract:

For ideal observation environments such as high signal-to-noise ratio (SNR), complete echoes and steadily moving targets, the available imaging techniques are mature to obtain high-resolution and well-focused inverse synthetic aperture radar (ISAR) images. However, in practical situations, factors such as azimuth sparse observation, low SNR, and random phase errors will reduce the performance of traditional algorithms or invalidate them. Based on the sparse Bayesian learning theory, this paper establishes the sparse observation model of ISAR and constructs the hierarchical probabilistic model by introducing the non-parametric Beta process prior. Then, Gibbs sampling and maximum likelihood estimation are utilized iteratively to estimate the ISAR image and the random phase errors. Experiments have demonstrated that in low SNR and incomplete data scenarios, well-focused imaging can be obtained by the proposed method.

Key words: inverse synthetic aperture radar (ISAR), high resolution imaging, sparse Bayesian learning, Beta process

CLC Number:

TN958

Anlin XU, Yu ZHANG, Feng ZHOU. High resolution ISAR imaging based on Beta process[J]. Systems Engineering and Electronics, 2022, 44(6): 1873-1879.

Figures/Tables 7

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References 22

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成像算法	图像熵
OMP	2.449 6
L1	3.313 6
RVM	0.503 8
BPLR	0.069 5

成像算法	图像熵
OMP	1.871 0
L1	2.883 8
RVM	0.197 1
BPLR	0.070 5

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High resolution ISAR imaging based on Beta process

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