SAR稀疏成像模型參數(shù)自適應(yīng)選擇
發(fā)布時(shí)間：2018-11-08 11:18
【摘要】：合成孔徑雷達(dá)(SAR)稀疏成像模型中的參數(shù)選擇對(duì)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(diǎn)問題。已有參數(shù)選擇方法普遍存在適用于個(gè)別模型或者運(yùn)算量大的缺點(diǎn)�；谧畲蠛篁�(yàn)概率估計(jì)和貝葉斯推理,提出了一種無需額外先驗(yàn)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(hào)、噪聲方差的關(guān)系,避免了對(duì)數(shù)據(jù)進(jìn)行一系列的訓(xùn)練處理,因此極大地減小了計(jì)算量。仿真數(shù)據(jù)和實(shí)測數(shù)據(jù)處理表明,本文方法在實(shí)現(xiàn)了較為精確的參數(shù)優(yōu)化選擇的前提下,其計(jì)算量遠(yuǎn)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(diǎn)實(shí)驗(yàn)室;
【基金】：國家自然科學(xué)基金(編號(hào):61501473,61490693,61490692)~~
【分類號(hào)】：TN957.52
本文編號(hào)：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(diǎn)實(shí)驗(yàn)室;
【基金】：國家自然科學(xué)基金(編號(hào):61501473,61490693,61490692)~~
【分類號(hào)】：TN957.52
【參考文獻(xiàn)】
相關(guān)期刊論文 前1條
1 陳國新;陳生昌;;位場數(shù)據(jù)重構(gòu)的l_p范數(shù)稀疏約束正則化方法[J];浙江大學(xué)學(xué)報(bào)(工學(xué)版);2014年04期
【共引文獻(xiàn)】
相關(guān)期刊論文 前1條
1 孫豆;邢世其;李永禎;代大海;;SAR稀疏成像模型參數(shù)自適應(yīng)選擇[J];遙感學(xué)報(bào);2017年04期
【二級(jí)參考文獻(xiàn)】
相關(guān)期刊論文 前5條
1 王薇;韓波;唐錦萍;;地震波形反演的稀疏約束正則化方法[J];地球物理學(xué)報(bào);2013年01期
2 曹靜杰;王彥飛;楊長春;;地震數(shù)據(jù)壓縮重構(gòu)的正則化與零范數(shù)稀疏最優(yōu)化方法[J];地球物理學(xué)報(bào);2012年02期
3 王萬銀;邱之云;;一種穩(wěn)定的位場數(shù)據(jù)最小曲率網(wǎng)格化方法研究[J];地球物理學(xué)進(jìn)展;2011年06期
4 焦李成;楊淑媛;劉芳;侯彪;;壓縮感知回顧與展望[J];電子學(xué)報(bào);2011年07期
5 郭良輝,孟小紅,郭志宏,劉國峰,常君勇,于更新;地球物理不規(guī)則分布數(shù)據(jù)的空間網(wǎng)格化法[J];物探與化探;2005年05期
，本文編號(hào)：2318328
本文鏈接：http://www.wukwdryxk.cn/kejilunwen/xinxigongchenglunwen/2318328.html
最近更新
教材專著
