[1]高伟伟,申丽然.时变AR模型阶数确定与系数估计的方法[J].应用科技,2010,37(11):30-34.[doi:10.3969/j.issn.1009-671X.2010.11.008]
 GAO Wei-wei,SHEN-liran.Order determination and parameter estimation of the time-varying AR model[J].Applied science and technology,2010,37(11):30-34.[doi:10.3969/j.issn.1009-671X.2010.11.008]
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时变AR模型阶数确定与系数估计的方法(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第37卷
期数:
2010年11期
页码:
30-34
栏目:
现代电子技术
出版日期:
2010-11-05

文章信息/Info

Title:
Order determination and parameter estimation of the time-varying AR model
文章编号:
1009- 671X(2010)11- 0030- 05
作者:
高伟伟申丽然
(哈尔滨工程大学 信息与通信工程学院,黑龙江 哈尔滨 150001)
Author(s):
GAO Wei-weiSHEN-liran
(College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China)
关键词:
TVAR时变参数模型非平稳信号时变参数估计
Keywords:
TVAR time-varying parameter model non-stationary signal time-varying parameter estimation
分类号:
TN911.6
DOI:
10.3969/j.issn.1009-671X.2010.11.008
文献标志码:
A
摘要:
研究了用时变自回归(TVAR)模型对非平稳信号建模的方法.对该模型进行详细分析,探讨了参数模型辨识存在的2大问题:模型阶数的确定和基函数的选择.基于现定阶准则只适用于短时平稳信号的分析,所以利用具有时变特性的信息理论准则(information theoretic criteria,ITC)来确定模型的阶数.通过引入基函数,利用最小二乘算法对模型系数进行估计,从而将非平稳信号的时变模型转化为线性时不变模型,并比较了几种基函数的拟合性能.证明了由于墨西哥草帽小波基函数具有良好的时频特性并且在使用时无需预知信号的先验信息,从而优于其他传统的基函数.
Abstract:
The application of time-varying autoregressive(TVAR) modeling approach to non-stationary signals was studied. The model was analyzed and two major problems existed in parametric model identification were discussed: the determination of model order and the selection of primary function. The commonly used order determination criterion at present only applies to analysis of short time stationary signal, so the information theoretical criteria (ITC) was used to determine the order of the model. The parameters were estimated by using the recursive least square algorithm of the primary function, and thereby the time-varying model of non-stationary signals can be translated into a linear time-invariable problem. Several kinds of primary functions were analyzed for their fitting functions, which proved that the Mexican hat function has good time-frequency characteristics and it does not need prior information when using the function, so it is better than other traditional primary functions.

参考文献/References:

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备注/Memo

备注/Memo:
基金项目:国家自然科学基金资助项目(60803087).
作者简介:高伟伟(1985-),女,硕士研究生,主要研究方向:语音增强,E-mail:gaoweiwei@hrbeu.edu.cn.
更新日期/Last Update: 2010-12-03