SAR稀疏成像模型參數(shù)自適應(yīng)選擇
發(fā)布時間：2018-11-08 11:18
【摘要】：合成孔徑雷達(SAR)稀疏成像模型中的參數(shù)選擇對于SAR稀疏成像的性能有重要影響,也是當(dāng)前SAR稀疏成像研究中的難點問題。已有參數(shù)選擇方法普遍存在適用于個別模型或者運算量大的缺點。基于最大后驗概率估計和貝葉斯推理,提出了一種無需額外先驗信息的自適應(yīng)參數(shù)選擇方法,所有需要的參數(shù)都可從已知的數(shù)據(jù)中獲取。通過推導(dǎo)得到模型參數(shù)與信號、噪聲方差的關(guān)系,避免了對數(shù)據(jù)進行一系列的訓(xùn)練處理,因此極大地減小了計算量。仿真數(shù)據(jù)和實測數(shù)據(jù)處理表明,本文方法在實現(xiàn)了較為精確的參數(shù)優(yōu)化選擇的前提下,其計算量遠低于貝葉斯信息論準(zhǔn)則、L-曲線等已有參數(shù)選擇方法。
[Abstract]:The selection of parameters in the (SAR) sparse imaging model of synthetic Aperture Radar (SAR) plays an important role in the performance of SAR sparse imaging, and it is also a difficult problem in the research of SAR sparse imaging. The existing parameter selection methods generally have the disadvantage of being suitable for individual models or having a large amount of computation. Based on maximum posteriori probability estimation and Bayesian reasoning, an adaptive parameter selection method without additional prior information is proposed. All required parameters can be obtained from known data. By deducing the relationship between the model parameters and the signal and noise variance, a series of training processing is avoided, so the computation is greatly reduced. The processing of simulation data and measured data shows that the computational complexity of this method is much lower than that of Bayesian information theory criterion and L- curve method on the premise of accurate parameter selection.
【作者單位】： 國防科技大學(xué)電子信息系統(tǒng)復(fù)雜電磁環(huán)境效應(yīng)國家重點實驗室;
【基金】：國家自然科學(xué)基金(編號:61501473,61490693,61490692)~~
【分類號】：TN957.52
本文編號：2318328
[Abstract]:The selection of parameters in the (SAR) sparse imaging model of synthetic Aperture Radar (SAR) plays an important role in the performance of SAR sparse imaging, and it is also a difficult problem in the research of SAR sparse imaging. The existing parameter selection methods generally have the disadvantage of being suitable for individual models or having a large amount of computation. Based on maximum posteriori probability estimation and Bayesian reasoning, an adaptive parameter selection method without additional prior information is proposed. All required parameters can be obtained from known data. By deducing the relationship between the model parameters and the signal and noise variance, a series of training processing is avoided, so the computation is greatly reduced. The processing of simulation data and measured data shows that the computational complexity of this method is much lower than that of Bayesian information theory criterion and L- curve method on the premise of accurate parameter selection.
【作者單位】： 國防科技大學(xué)電子信息系統(tǒng)復(fù)雜電磁環(huán)境效應(yīng)國家重點實驗室;
【基金】：國家自然科學(xué)基金(編號:61501473,61490693,61490692)~~
【分類號】：TN957.52
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