基于塊稀疏貝葉斯模型的ISAR成像方法
doi: 10.11999/JEIT141624 cstr: 32379.14.JEIT141624
基金項(xiàng)目:
國(guó)家自然科學(xué)基金(61171133)，國(guó)家自然科學(xué)青年基金(61101182, 61302148)
ISAR Imaging Method Based on the Bayesian Group-sparse Modeling
Funds:
The National Natural Science Foundation of China (61171133)
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摘要: 傳統(tǒng)ISAR稀疏成像主要針對(duì)獨(dú)立散射點(diǎn)散射系數(shù)的重構(gòu)問(wèn)題，然而實(shí)際情況下目標(biāo)散射點(diǎn)之間并不是獨(dú)立存在的，而是以區(qū)域或塊的形式存在，在該情形下利用常用的稀疏重構(gòu)算法并不能完全地刻畫(huà)塊狀目標(biāo)的真實(shí)結(jié)構(gòu)，因此該文考慮采用塊稀疏重構(gòu)算法進(jìn)行目標(biāo)散射系數(shù)重建?；趬K稀疏貝葉斯模型和變分推理的重構(gòu)方法(VBGS)，包含了稀疏貝葉斯學(xué)習(xí)(SBL)方法中參數(shù)學(xué)習(xí)的優(yōu)點(diǎn)，其利用分層的先驗(yàn)分布來(lái)表征未知信號(hào)的稀疏塊狀信息，因而相對(duì)于現(xiàn)有的恢復(fù)算法能夠更好地重建塊稀疏信號(hào)。該方法基于變分貝葉斯推理原理，根據(jù)觀測(cè)量能自動(dòng)地估計(jì)信號(hào)未知參數(shù)，而無(wú)需人工參數(shù)設(shè)置。針對(duì)稀疏塊狀目標(biāo)，該文結(jié)合壓縮感知(CS)理論將VBGS方法用于ISAR成像，仿真實(shí)驗(yàn)成像結(jié)果表明該方法優(yōu)于傳統(tǒng)的成像結(jié)果，適合于具有塊狀結(jié)構(gòu)的ISAR目標(biāo)成像。
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關(guān)鍵詞:
- 逆合成孔徑雷達(dá) /
- 塊稀疏模型 /
- 壓縮感知(CS) /
- 塊稀疏貝葉斯模型和變分推理(VBGS)
Abstract: The traditional sparse ISAR imaging method mainly considers the recovery of coefficients on individual scatters. However, in the practice situation, the target scatters presented by blocks or groups do not emerge on individual. In this case, the usual sparse recover algorithm can not depict the shape of real target, thus, the group-sparse recover approaches are adopted to reconstruct the coefficients of target scatters. The recovery method based on the Bayesian Group-Sparse modeling and Variational inference (VBGS) uses a hierarchical construction of a general signal prior to model the group sparse signals and contain the merit of Sparse Bayesian Learning (SBL) on parameters learning, as a result, it can reconstruct the group sparse signal better than the usual recover algorithm. The VBGS method uses the variational Bayesian inference approach to estimate the parameters of the unknown signal automatically and does not require the parameter-tuning procedures. Considering the sparse group target, this paper combines the Compress Sensing (CS) theory and the VBGS method to reconstruct the ISAR image. The result of experiments show that the proposed method can improve the imaging accuracy compared with traditional algorithm, and can fit to reconstruct the image of ISAR target which has group structure. -
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