A new kalman filter-based power spectral density estimation for nonstationary pressure signals

被引：2
|
作者：
Zhang, Z. G. [1 ]
Lau, W. Y. [1 ]
Chan, S. C. [1 ]
机构：
[1] Univ Hong Kong, Dept Elect & Elect Engn, Pokfulam Rd, Hong Kong, Peoples R China
来源：
2006 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS 1-11, PROCEEDINGS | 2006年
关键词：
D O I：
10.1109/ISCAS.2006.1692911
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a new Kalman filter-based power spectral density estimation (PSD) algorithm for nonstationary pressure signals. The pressure signal is assumed to be an autoregressive (AR) process, and a stochastically perturbed difference equation constraint model is used to describe the dynamics of the AR coefficients. The proposed Kalman filter frame uses variable number of measurements to estimate the time-varying AR coefficients and yield the PSD estimation with better time-frequency resolution. Simulation results show that the proposed algorithm achieves a better time-frequency resolution than conventional algorithms for nonstationary pressure signals.
引用
收藏
页码：1619 / +
页数：2
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