基于稀疏貝葉斯方法的脈間捷變頻ISAR成像技術(shù)研究
doi: 10.11999/JEIT140315 cstr: 32379.14.JEIT140315
基金項(xiàng)目:
國家自然科學(xué)基金(61171133)和國家自然科學(xué)青年基金(61101182, 61302148)資助課題
The Interpulse Frequency Agility ISAR Imaging Technology Based on Sparse Bayesian Method
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摘要: 傳統(tǒng)捷變頻成像方法具有高旁瓣、低分辨率的缺點(diǎn)。鑒于捷變頻ISAR回波信號(hào)的稀疏性，該文基于原始數(shù)據(jù)的2維壓縮感知方案，在貝葉斯原理框架下，用稀疏貝葉斯算法方差成分?jǐn)U張壓縮方法(ExCoV)實(shí)現(xiàn)捷變頻ISAR像的重建。貝葉斯框架下的稀疏重構(gòu)算法考慮了稀疏信號(hào)的先驗(yàn)信息以及測量過程中的加性噪聲，因而能夠更好地重建目標(biāo)系數(shù)。作為一種新的稀疏貝葉斯算法，ExCoV不同于稀疏貝葉斯學(xué)習(xí)(SBL)算法中賦予所有的信號(hào)元素各自的方差分量參數(shù)，ExCoV方法僅僅賦予有重要意義的信號(hào)元素不同的方差分量，并擁有比SBL方法更少的參數(shù)，克服了SBL算法參數(shù)多時(shí)效性差的缺點(diǎn)。仿真結(jié)果表明，該方法能克服傳統(tǒng)捷變頻成像缺點(diǎn)，并能夠?qū)崿F(xiàn)低信噪比條件下的2維高精度成像。
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關(guān)鍵詞:
- ISAR /
- 捷變頻 /
- 壓縮感知 /
- 稀疏貝葉斯學(xué)習(xí)算法 /
- 方差成分?jǐn)U張壓縮方法(ExCoV)
Abstract: Traditional frequency agility ISAR imaging method suffers from high sidelobe and low resolution. To improve the resolution, by exploiting the sparsity of targets in the received echo, this paper uses the sparse Bayesian algorithm, namely Expansion-Compression Variance-component based method (ExCoV), to reconstruct the ISAR image from the original Compressed Sensing (CS) ISAR data. By taking into account of the prior information of the sparse signal and the additive noise encountered in the measurement process, the sparse recover algorithm under the Bayesian framework can reconstruct the scatter coefficient better than the traditional methods. Different from the Sparse Bayesian Learning (SBL) endowing variance-components to all elements, the ExCoV only endows variance-components to the significant signal elements. This leads to much less parameters and faster implementation of the ExCoV than the SBL. The simulation results indicate that it can conquer the problem brought by traditional methods and achieve high precision agility ISAR imaging under the low SNR. -
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