摘要
将复杂的非平稳随机信号划为分段平稳随机信号进行处理,以信号自相关函数反映信号特征。而自相关函数是数字图像频谱分析的基础,可作为图像清晰度评价函数,并有助于寻找有效的信号正交基。为精确有效地表示分段平稳随机信号,在分析ARMA模型、分段平稳随机过程和Markov过程的基础上,建立多参数的自相关函数估计模型,其提高了逼近效果,可适应变化复杂的非平稳信号。计算机仿真表明,该模型逼近误差显著下降。
Non-stationary stochastic signal was divided into piecewise stationary stochastic signal,and reflecting the sig-nal’s characteristics by autocorrelation function of the piecewise stationary stochastic signal.Generally,the autocorrela-tion function was the base of selecting signal base for signal representation.For expressing non-stationary stochastic sig-nal in a precise and effective way,based on the analysis of the natural characteristics of ARMA model and Markov proc-ess,a kind of multi-parameter estimation model of autocorrelation function for piecewise stationary stochastic process was proposed.The computational complexity was reduced,and the approximation effect was improved.Furthermore,the multi-parameter estimation model could also be adapted to the complex non-stationary stochastic signal,The computer simulation demonstrates that the approximation error was decreased significantly.
出处
《通信学报》
EI
CSCD
北大核心
2011年第10期185-190,共6页
Journal on Communications
基金
国家科技重大专项基金资助项目(2012ZX03005012
2009ZX03003-007
2011ZX03005-004-03)~~
关键词
随机过程
分段平稳
非线性逼近
自相关
stochastic process
piecewise stationary process
nonlinear approximation
auto correlation
