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邱天爽
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教授   博士生导师   硕士生导师

性别: 男

毕业院校: 大连理工大学

学位: 博士

所在单位: 生物医学工程学院

学科: 信号与信息处理. 生物医学工程

办公地点: 大连理工大学创新园大厦

联系方式: 电子邮箱:qiutsh@dlut.edu.cn; 电话:15898159801

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Tracking performance and robustness analysis of Hurst estimators for multifractional processes

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论文类型: 期刊论文

发表时间: 2012-05-01

发表刊物: IET SIGNAL PROCESSING

收录刊物: SCIE、EI、Scopus

卷号: 6

期号: 3

页面范围: 213-226

ISSN号: 1751-9675

摘要: In this study, the authors focus on the tracking performance and the robustness of 12 sliding-windowed Hurst estimators for multifractional processes with linear trend local Holder exponent, noisy multifractional processes and multifractional processes with infinite second-order statistics. Four types of multifractional processes are synthesised to test the tracking performance and robustness of these 12 sliding-windowed Hurst estimators. They are (i) noise-free multifractional process; (ii) multifractional process corrupted by 30-dB signal-to-noise ratio (SNR) white Gaussian noise; (iii) multifractional process corrupted by 30-dB SNR impulse noise; and (iv) multifractional stable process, which has no finite second-order statistics. Furthermore, the standard error of different sliding-windowed Hurst estimators are calculated in order to quantify the accuracy and robustness. This study provides a guideline and principle in the selection of Hurst estimators for noise-free multifractional process, noise-corrupted multifractional process and multifractional process with infinite second-order statistics. The results of this analysis show that the sliding-windowed Kettani and Gubner's method provides the best-tracking performance for multifractional processes with linear trend local Holder exponent and good robustness to noise.

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