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基于SS過程的分?jǐn)?shù)低階時(shí)頻自回歸滑動(dòng)平均模型參數(shù)估計(jì)及時(shí)頻分布

龍俊波,  汪海濱

龍俊波, 汪海濱. 基于SS過程的分?jǐn)?shù)低階時(shí)頻自回歸滑動(dòng)平均模型參數(shù)估計(jì)及時(shí)頻分布[J]. 電子與信息學(xué)報(bào), 2016, 38(7): 1710-1716. doi: 10.11999/JEIT151066
引用本文: 龍俊波, 汪海濱. 基于SS過程的分?jǐn)?shù)低階時(shí)頻自回歸滑動(dòng)平均模型參數(shù)估計(jì)及時(shí)頻分布[J]. 電子與信息學(xué)報(bào), 2016, 38(7): 1710-1716. doi: 10.11999/JEIT151066
LONG Junbo, WANG Haibin. Parameter Estimation and Time-frequency Distribution of Fractional Lower Order Time-frequency Auto-regressive Moving Average Model Algorithm Based on SS Process[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1710-1716. doi: 10.11999/JEIT151066
Citation: LONG Junbo, WANG Haibin. Parameter Estimation and Time-frequency Distribution of Fractional Lower Order Time-frequency Auto-regressive Moving Average Model Algorithm Based on SS Process[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1710-1716. doi: 10.11999/JEIT151066

基于SS過程的分?jǐn)?shù)低階時(shí)頻自回歸滑動(dòng)平均模型參數(shù)估計(jì)及時(shí)頻分布

doi: 10.11999/JEIT151066
  • 1.

    (九江學(xué)院電子工程學(xué)院 九江 332005) ②(九江學(xué)院信息科學(xué)與技術(shù)學(xué)院 九江 332005)

基金項(xiàng)目: 

國家自然科學(xué)基金(61261046, 61362038)，江西省自然科學(xué)基金(20142BAB207006)，江西省教育廳科技基金(GJJ14738, GJJ14739)

Parameter Estimation and Time-frequency Distribution of Fractional Lower Order Time-frequency Auto-regressive Moving Average Model Algorithm Based on SS Process

  • 1.

    (College of Electronic and Engineering, Jiujiang University, Jiujiang 332005, China)

  • 2.

    (College of Information Science and Technology, Jiujiang University, Jiujiang 332005, China)

Funds: 

The National Natural Science Foundation of China (61261046, 61362038), The Natural Science Foundation of Jiangxi Province (20142BAB207006), The Research Foundation of Education Bureau of Jiangxi Province (GJJ14738, GJJ14739)

  • 摘要: 針對SS過程下時(shí)頻自回歸滑動(dòng)平均(TFARMA)模型分析方法的退化，該文用分?jǐn)?shù)低階共變?nèi)〈A相關(guān)提出了分?jǐn)?shù)低階時(shí)頻自回歸滑動(dòng)平均(FLO-TFARMA)模型的概念，并推導(dǎo)了模型參數(shù)的求解方法。在此基礎(chǔ)上，給出了FLO- TFARMA模型時(shí)頻譜估計(jì)算法，和已有的TFARMA模型時(shí)頻譜算法進(jìn)行了詳細(xì)的比較。計(jì)算機(jī)仿真結(jié)果表明，在SS過程環(huán)境下，所提出的FLO-TFARMA時(shí)頻譜明顯優(yōu)于TFARMA時(shí)頻譜，尤其是當(dāng)參數(shù)較小時(shí)，F(xiàn)LO-TFARMA時(shí)頻譜優(yōu)勢更明顯。
    Abstract: The performances of Time-Frequency Auto-Regressive Moving Average (TFARMA) model method degenerate underSS distribution environment. Hence, Fractional Lower Order Time-Frequency Auto- Regressive Moving Average (FLO-TFARMA) model algorithm based on fractional lower order covariant is proposed, the parameters estimation of FLO-TFARMA model is introduced, time-frequency distribution based on FLO-TFARMA model is given, FLO-TFARMA model algorithm are compared with the existing TFARMA algorithm in detail. The simulation results show that FLO-TFARMA model method have better performance than TFARMA model method underSS distribution environment, and the time-frequency spectrum of FLO- TFARMA method is more obvious when the parameter is smaller.
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出版歷程
  • 收稿日期:  2015-09-21
  • 修回日期:  2016-04-28
  • 刊出日期:  2016-07-19

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