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

LONG Junbo; WANG Haibin

doi:10.11999/JEIT151066

Volume 38 Issue 7

Jul. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(7): 1710-1716

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

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

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

doi: 10.11999/JEIT151066

LONG Junbo¹,
WANG Haibin²

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)

Received Date: 2015-09-21
Rev Recd Date: 2016-04-28
Publish Date: 2016-07-19

Abstract

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.
- Signal processing,
- stable distribution,
- Non-stationary signal,
- Time-frequency distribution,
- Auto- Regressive Moving Average (ARMA),
- Yule-Walker equations,

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References(24)

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