可变先验贝叶斯学习稀疏SAR成像

doi:10.12305/j.issn.1001-506X.2021.07.07

Abstract

Abstract:

Aiming at the problem of prior solidification and over-fitting imaging results easily of traditional Bayesian learning imaging algorithms in sparse synthetic aperture radar (SAR) imagery, a varying imaging prior Bayes (VIP-Bayes) learning algorithm is proposed. Firstly, the generalized Gaussian distribution variable dynamic prior is introduced, which can realize the dynamic and flexible representation of the target scattering prior knowledge. Then, in the framework of Bayesian inference, a hierarchical Bayesian model is introduced to derive the posterior distribution. Finally, aiming at the problerm, which the conventional Gibbs algorithm cannot sample the obtained complicated posterior distribution, the Hamiltonian Monte Carlo (HMC) sampling algorithm is introduced to solve the problem. In addition, considering the HMC algorithm can't sample for non-smooth posterior distributions. This paper introduces a proximal operator to approximate the gradient, and obtains the proximal-HMC (P-HMC) sampling algorithm. P-HMC algorithm can effectively solve the problem of non-smooth posterior sampling, for this reason, it can realize sparse imaging based on VIP-Bayes learning. The effectiveness of the algorithm is verified by using the simulation data, then select SAR raw data to conduct a variety of algorithm imaging comparison experiments. Finally, phase transition diagram is applied to quantitatively analyze the algorithm imaging performance. In a word, all above experiments verifies the practicability and superiority of the proposed algorithm.

Key words: synthetic aperture radar (SAR), Bayes learning, Hamiltonian Monte Carlo (HMC) algorithm, generalized Gaussian distribution (GGD)

CLC Number:

TN957

Xiaoyun SHEN, Xianhua LIAO, Weitian SUN, Yabo XIA, Lei YANG. Sparse SAR imaging based on varying prior Bayes learning[J]. Systems Engineering and Electronics, 2021, 43(7): 1781-1790.

Figures/Tables 10

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项目	参数值
机载雷达高度/m	4 000
飞行速率/(m/s)	100
脉冲重复频率/Hz	100
脉冲宽度/μs	5
载波频率/GHz	10
发射信号带宽/MHz	150
天线方位向尺寸/m	1.7
距离向分辨率/m	1
方位向分辨率/m	0.85

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Sparse SAR imaging based on varying prior Bayes learning

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