基于稀疏貝葉斯學(xué)習(xí)的無源雷達(dá)高分辨成像
doi: 10.11999/JEIT140899
基金項(xiàng)目:
國(guó)家自然科學(xué)基金(6117255, 61401140)和國(guó)家863計(jì)劃項(xiàng)目(2012AA122903)資助課題
High-resolution Imaging of Passive Radar Based on Sparse Bayesian Learning
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摘要: 針對(duì)無源雷達(dá)壓縮感知成像，該文提出一種基于稀疏貝葉斯學(xué)習(xí)的高分辨成像算法?；谝淮慰炫哪Ｊ较碌臒o源雷達(dá)回波模型，文中首先考慮目標(biāo)散射系數(shù)的統(tǒng)計(jì)特性及其對(duì)微波頻率的依賴關(guān)系，將無源雷達(dá)成像轉(zhuǎn)化為MMV(Multiple Measurement Vector)聯(lián)合稀疏優(yōu)化問題；然后對(duì)目標(biāo)建立了級(jí)聯(lián)形式的稀疏先驗(yàn)?zāi)Ｐ?，并利用稀疏貝葉斯學(xué)習(xí)技術(shù)進(jìn)行求解。相比之前基于目標(biāo)確定性假設(shè)的稀疏恢復(fù)方法，所提算法更好地利用了目標(biāo)的統(tǒng)計(jì)先驗(yàn)信息，具有能夠自適應(yīng)調(diào)整參數(shù)(目標(biāo)模型參數(shù)和未知噪聲功率)和高分辨反演目標(biāo)等優(yōu)點(diǎn)。仿真結(jié)果驗(yàn)證了該算法的有效性。
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關(guān)鍵詞:
- 無源雷達(dá) /
- 高分辨率成像 /
- 稀疏貝葉斯學(xué)習(xí) /
- 聯(lián)合稀疏優(yōu)化
Abstract: This paper presents a high-resolution imaging method based on Sparse Bayesian Learning (SBL) for passive radar compressed sensing imaging. Under the one-snapshot echo model, the proposed method firstly takes account of the frequency-dependent statistics of the target scattering centers, and changes passive radar imaging into a joint Multiple Measurement Vector (MMV) sparse optimization problem. Further, a hierarchical Bayesian framework for sparsity-inducing priori of the target is established, then the MMV problem is efficiently solved by utilizing the SBL theory. Unlike the previous sparse recovery algorithms relying on the deterministic assumption of the target, the proposed method makes a better use of the target prior information, and has the advantages of adaptively estimating parameters (including the parameters in the priori model of the target, and the unknown noise power) as well as the high-resolution imaging, etc.. Simulation results show the effectiveness of the proposed method. -
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