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基于空域稀疏性的自適應(yīng)頻譜檢測(cè)算法

于宏毅 程標(biāo) 胡赟鵬 沈智翔

于宏毅, 程標(biāo), 胡赟鵬, 沈智翔. 基于空域稀疏性的自適應(yīng)頻譜檢測(cè)算法[J]. 電子與信息學(xué)報(bào), 2016, 38(7): 1703-1709. doi: 10.11999/JEIT151030
引用本文: 于宏毅, 程標(biāo), 胡赟鵬, 沈智翔. 基于空域稀疏性的自適應(yīng)頻譜檢測(cè)算法[J]. 電子與信息學(xué)報(bào), 2016, 38(7): 1703-1709. doi: 10.11999/JEIT151030
YU Hongyi, CHENG Biao, HU Yunpeng, SHEN Zhixiang. Adaptive Spectrum Detection Algorithm Based on Spatial Sparsity[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1703-1709. doi: 10.11999/JEIT151030
Citation: YU Hongyi, CHENG Biao, HU Yunpeng, SHEN Zhixiang. Adaptive Spectrum Detection Algorithm Based on Spatial Sparsity[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1703-1709. doi: 10.11999/JEIT151030

基于空域稀疏性的自適應(yīng)頻譜檢測(cè)算法

doi: 10.11999/JEIT151030
基金項(xiàng)目: 

國家自然科學(xué)基金(61501517)

Adaptive Spectrum Detection Algorithm Based on Spatial Sparsity

Funds: 

The National Natural Science Foundation of China (61501517)

  • 摘要: 現(xiàn)有的頻譜檢測(cè)算法沒有充分利用信號(hào)在角度維的稀疏性質(zhì)。該文根據(jù)角度維的稀疏特性建立信號(hào)模型,通過稀疏貝葉斯學(xué)習(xí)(Sparse Bayesian Learning, SBL)算法解決稀疏信號(hào)的重構(gòu)問題,并在迭代過程中引入二元假設(shè)檢驗(yàn)思想,推導(dǎo)出一種自適應(yīng)門限的選取策略,把傳統(tǒng)的重構(gòu)算法轉(zhuǎn)化為一個(gè)針對(duì)不同來波方向的信號(hào)檢測(cè)問題。該算法能夠在恒虛警概率下對(duì)多信號(hào)進(jìn)行全盲檢測(cè),同時(shí)實(shí)現(xiàn)信號(hào)來波方向的精確估計(jì)。實(shí)驗(yàn)結(jié)果證明,自適應(yīng)判決方法能夠有效地提高稀疏重構(gòu)算法的重構(gòu)精度,降低運(yùn)算復(fù)雜度,參數(shù)估計(jì)精度和信號(hào)檢測(cè)性能相比于現(xiàn)有算法得到明顯的提升。
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出版歷程
  • 收稿日期:  2015-09-10
  • 修回日期:  2016-01-22
  • 刊出日期:  2016-07-19

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