基于貝葉斯壓縮感知的ISAR自聚焦成像
doi: 10.11999/JEIT150235 cstr: 32379.14.JEIT150235
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2.
(中國科學(xué)技術(shù)大學(xué)電磁空間信息重點實驗室 合肥 230027) ②(中國衛(wèi)星海上測控部 江陰 214431)
基金項目:
國家自然科學(xué)基金(61172155, 61401140)和國家863計劃項目(2013AA122903)
An Autofocus Imaging Method for ISAR Based on Bayesian Compressive Sensing
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2.
(Key Laboratory of Electromagnetic Space Information, University of Science and Technology of China, Hefei 230027, China)
Funds:
The National Natural Science Foundation of China (61172155, 61401140)
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摘要: 針對ISAR自聚焦成像，該文提出一種基于貝葉斯壓縮感知的高分辨率成像算法。首先利用目標(biāo)圖像的稀疏特性構(gòu)建級聯(lián)形式的稀疏先驗?zāi)Ｐ?，同時將相位誤差建模為均勻分布模型；然后基于最大后驗準(zhǔn)則，依據(jù)貝葉斯壓縮感知理論交替迭代求解目標(biāo)圖像和相位誤差。與傳統(tǒng)稀疏方法相比，所提算法進(jìn)一步利用了目標(biāo)圖像的聯(lián)合稀疏信息，將ISAR CS成像轉(zhuǎn)化為MMV聯(lián)合稀疏優(yōu)化問題的求解，可以有效改善自聚焦的精度以及成像質(zhì)量。仿真結(jié)果驗證了該算法的有效性。
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關(guān)鍵詞:
- 逆合成孔徑雷達(dá) /
- 自聚焦技術(shù) /
- 高分辨成像 /
- 貝葉斯壓縮感知
Abstract: For Inverse Synthetic Aperture Radar (ISAR) autofocus imaging, this paper proposes a high-resolution imaging method based on Bayesian Compressed Sensing (BCS). Firstly, according to the sparsity characteristics of target image, a sparse model with the hierarchical framework is established, which can achieve better approximation to the original l0 norm. Then, the phase errors are assumed to obey the uniform distribution. Next, following the criterion of Maximum A Posteriori (MAP), target image and phase errors are solved using alternate iteration based on BCS theory. Compared with traditional methods, the proposed method further combines the joint sparse information of target image, and converts the ISAR CS imaging into solving a joint Multiple Measurement Vector (MMV) sparse optimization problem, which can improve both the autofocus precision and the imaging quality efficiently. Simulation results show the effectiveness of the proposed method. -
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