| 基于SS过程的分数低阶时频自回归滑动平均模型参数估计及时频分布 |
| |
| 引用本文： | 龙俊波,汪海滨.基于SS过程的分数低阶时频自回归滑动平均模型参数估计及时频分布[J].电子与信息学报,2016,38(7):1710-1716. |
| |
| 作者姓名： | 龙俊波 汪海滨 |
| |
| 作者单位： | 1.(九江学院电子工程学院 九江 332005) ②(九江学院信息科学与技术学院 九江 332005) |
| |
| 基金项目： | 国家自然科学基金(61261046, 61362038)，江西省自然科学基金(20142BAB207006)，江西省教育厅科技基金(GJJ14738, GJJ14739) |
| |
| 摘 要： | 针对SS过程下时频自回归滑动平均(TFARMA)模型分析方法的退化，该文用分数低阶共变取代二阶相关提出了分数低阶时频自回归滑动平均(FLO-TFARMA)模型的概念，并推导了模型参数的求解方法。在此基础上，给出了FLO- TFARMA模型时频谱估计算法，和已有的TFARMA模型时频谱算法进行了详细的比较。计算机仿真结果表明，在SS过程环境下，所提出的FLO-TFARMA时频谱明显优于TFARMA时频谱，尤其是当参数较小时，FLO-TFARMA时频谱优势更明显。
|
| 关 键 词： | 信号处理 信号处理 稳定分布 非平稳信号 时频分布 自回归滑动平均 尤拉沃克方程 |
| 收稿时间： | 2015-09-21 |
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.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. |
| |
| Authors: | LONG Junbo WANG Haibin |
| |
| Affiliation: | 1.(College of Electronic and Engineering, Jiujiang University, Jiujiang 332005, China)2.(College of Information Science and Technology, Jiujiang University, Jiujiang 332005, China) |
| |
| 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. |
| |
| Keywords: | |
|
| 点击此处可从《电子与信息学报》浏览原始摘要信息 |
|
点击此处可从《电子与信息学报》下载全文 |
