一種改進的分段平穩(wěn)隨機過程的參數(shù)估計方法
An advanced method to estimate parameters of piecewise stationary stochastic process
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摘要: 將非平穩(wěn)隨機信號劃分為分段平穩(wěn)隨機信號進行處理,為非平穩(wěn)隨機信號的研究提供的一種新的分析方法。為最優(yōu)地將非平穩(wěn)隨機信號劃分為分段平穩(wěn)隨機信號,Djuric等人用 Bayes方法,通過遞推多維條件分布概率來估計最優(yōu)劃分參數(shù)值,但計算相當復(fù)雜。本文在研究 AR模型本身的一些特性的基礎(chǔ)上,通過直接遞推多維聯(lián)合分布概率來估計最優(yōu)劃分參數(shù),大大地減少了計算量。
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關(guān)鍵詞:
- 非平穩(wěn)隨機信號; 分段平穩(wěn)隨機信號; AR模型; 參數(shù)估計
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. -
P.M. Djuric, et al., Segmentation of nonstationary signal, Proc. of IEEE ICASSP, San Franciso, 1992,5, 161-164.[2]王文華等,分段平穩(wěn)隨機過程的參數(shù)估計方法,電子科學(xué)學(xué)刊,1997,19(3),311-317.[3]王宏禹,非平穩(wěn)隨機信號分析與處理,北京,國防工業(yè)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]田錚,動態(tài)數(shù)據(jù)處理的理論與方法-時間序列分析,西安,西北工業(yè)大學(xué)出版社,1995,89-92. -
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