2020
DOI: 10.1109/access.2020.2965973
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A Bayesian Angular Superresolution Method With Lognormal Constraint for Sea-Surface Target

Abstract: Maximum a posteriori (MAP) approach, based on Bayesian criterion, is proposed to overcome the low azimuth resolution in real-aperture imaging. The essence of this approach is to use the statistical characteristics of the imaging background and target to invert the real target scene. This paper presents a deconvolution method based on Maximum a posteriori (MAP) criterion, which combines the Rayleigh distribution and Lognormal distribution, to realize high angular resolution for sea-surface target. Firstly, Rayl… Show more

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Cited by 16 publications
(8 citation statements)
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“…To enhance its azimuth resolution, the existing azimuth superresolution deconvolution methods can be divided into following three aspects: 1) array signal processing methods [11], [12], [13], [14], [15], [16]; 2) Bayesian methods [17], [18], [19], [20], [21], [22], [23], [24] and 3) regularization methods [25], [26], [27], The authors are with the School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: xingyu_tuo@163.com; mdq_uestc@163.com; yinzhang@uestc.edu.cn; yongchaozhang@uestc.edu.cn; yulinhuang@uestc. edu.cn; jyyang@uestc.edu.cn).…”
Section: Introductionmentioning
confidence: 99%
See 2 more Smart Citations
“…To enhance its azimuth resolution, the existing azimuth superresolution deconvolution methods can be divided into following three aspects: 1) array signal processing methods [11], [12], [13], [14], [15], [16]; 2) Bayesian methods [17], [18], [19], [20], [21], [22], [23], [24] and 3) regularization methods [25], [26], [27], The authors are with the School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: xingyu_tuo@163.com; mdq_uestc@163.com; yinzhang@uestc.edu.cn; yongchaozhang@uestc.edu.cn; yulinhuang@uestc. edu.cn; jyyang@uestc.edu.cn).…”
Section: Introductionmentioning
confidence: 99%
“…Bayesian methods assume different statistical distribution characteristics of the target and interference to reduce the imaging error and improve the azimuth resolution. The distribution functions of the target can be selected as Laplace distribution for sparse ground surface targets [19], [20], lognormal distribution for sparse sea surface targets [21], [22], and generalized Gaussian distribution for complex targets by controlling its statistic parameter [23], [24]. The distribution functions of interference can be determined as Rayleigh distribution for sea background [22], and Gaussian distribution for noise background [19].…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Moreover, Yang and Zhang et al proposes the total variation (TV) (Zhang et al, 2020a;Zhang et al, 2020b) based method to describe the scene information, which performs well for the in preserving the contour of the target. To make full use of the prior information of the forward-looking imaging scene, Yang, Chen and Li et al propose the Bayesian framework (Yang et al, 2020;Chen et al, 2022;Li et al, 2022) based forward-looking methods, which converts forward-looking into the optimization estimation. The key point of these methods is to well establish the imaging scene model.…”
Section: Introductionmentioning
confidence: 99%
“…In this paper, on the basis of the forward-looking imaging model established previously (Richards, 1988;Kusama et al, 1990;Dropkin and Ly, 1997;Chen et al, 2015;Huang et al, 2015;Zhang et al, 2018a;Zhang et al, 2018b;Li et al, 2019;Zhang et al, 2019;Zhang et al, 2020a;Zhang et al, 2020b;Yang et al, 2020;Tuo et al, 2021;Chen et al, 2022;Li et al, 2022), a data-driven airborne Bayesian superresolution imaging method via generalized gaussian distribution (GGD-Bayesian) for complex imaging scene is proposed. The main contribution of the paper is the forward-looking method for the automatic selection of the sparsity parameter.…”
Section: Introductionmentioning
confidence: 99%