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
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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)
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
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1.
(College of Electronic and Engineering, Jiujiang University, Jiujiang 332005, China)
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2.
(College of Information Science and Technology, Jiujiang University, Jiujiang 332005, China)
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)
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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。
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關(guān)鍵詞:
- 信號(hào)處理 /
- 信號(hào)處理 /
- 穩(wěn)定分布 /
- 非平穩(wěn)信號(hào) /
- 時(shí)頻分布 /
- 自回歸滑動(dòng)平均 /
- 尤拉沃克方程
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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