一种改进的分段平稳随机过程的参数估计方法
An advanced method to estimate parameters of piecewise stationary stochastic process
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摘要: 将非平稳随机信号划分为分段平稳随机信号进行处理,为非平稳随机信号的研究提供的一种新的分析方法。为最优地将非平稳随机信号划分为分段平稳随机信号,Djuric等人用 Bayes方法,通过递推多维条件分布概率来估计最优划分参数值,但计算相当复杂。本文在研究 AR模型本身的一些特性的基础上,通过直接递推多维联合分布概率来估计最优划分参数,大大地减少了计算量。Abstract: A new way to analysis nonstationary stochastic process is to divide it into piece-wise stationary stochastic process. Djuric(1992) used Bayes method to estimate the parameters, which can optimally divide the nonstationary stochastic process into stationary stochastic pro-cess. Some authors estimated the optimum parameters through calculating recursively the multivariate conditional likelihood function, which made the computation very complex. Bas-ing on some natural characteristics of AR. mode, a new recursive method is provided, which can improve the computation efficiently, to estimate the optimum parameters.
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P.M. Djuric, et al., Segmentation of nonstationary signal, Proc. of IEEE ICASSP, San Franciso, 1992,5, 161-164.[2]王文华等,分段平稳随机过程的参数估计方法,电子科学学刊,1997,19(3),311-317.[3]王宏禹,非平稳随机信号分析与处理,北京,国防工业出版社,1999,230-244.[4]P.M. Djutic, et al., Order selection of autoregressive models, IEEE Trans. on Signal Processing,1992.40(11) 2829-2833.[5]田铮,动态数据处理的理论与方法-时间序列分析,西安,西北工业大学出版社,1995,89-92. -
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