文件名称:signal
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产生一个随机信号和两个不同频率但频率间隔很小的正弦信号,要求对两信号之和进行如下分析:
(1) 求该随机信号的自相关性系数、自相关函数,画出对应的图形;
(2) 利用不同的参数建模方法求出两个随机信号的功率谱;
(3) 利用极大似然估计、递推最小二乘法等常用的参数估计方法估计所建模型,包括AR模型、MA模型和ARMA模型的的参数,阶次自定;并与Matlab工具箱里的一些建模函数的运算结果进行比较;
(4) 利用陷波滤波和MUSIC滤波方法对该信号的频谱进行估计;
(5) 利用Wiener滤波、LMS滤波对该含噪声的正弦信号进行去噪声处理;
(6) 假设该信号是一个飞行器的某个方向的线位移信号,可否利用Kalman滤波对该信号进行滤波?
(7) 利用高阶谱理论对该信号进行谱估计和相应的AR模型、MA模型和ARMA模型估计;
(8) 利用小波变换(变换函数可以直接用Matlab里的函数)对该信号进行去噪声处理,并和前面的去噪声方法进行比较。
-Produce a random signal and two different frequency but frequency interval small sine signals, required to make the following analysis of two signals:
(1) for the random signal from the correlation coefficients, the autocorrelation function and draw the corresponding graphics
(2) make use of different parameters modeling method for out two random signal power spectrum
(3) using maximum likelihood estimation, recursive least square method and common parameters estimation method estimates that the model, including AR model, MA model and the parameters of the ARMA model we, order time decided oneself With Matlab toolbox and some of the modeling function is used compared the
(4) use trapped wave filter and MUSIC of the signal filter method of spectrum to estimate
(5) use Wiener filtering, LMS filtering on the sinusoidal signal with noise to noise treatment
(6) assumes the signal is a vehicle of a certain direction of displacement signal line, can you use Kalman filtering on the
(1) 求该随机信号的自相关性系数、自相关函数,画出对应的图形;
(2) 利用不同的参数建模方法求出两个随机信号的功率谱;
(3) 利用极大似然估计、递推最小二乘法等常用的参数估计方法估计所建模型,包括AR模型、MA模型和ARMA模型的的参数,阶次自定;并与Matlab工具箱里的一些建模函数的运算结果进行比较;
(4) 利用陷波滤波和MUSIC滤波方法对该信号的频谱进行估计;
(5) 利用Wiener滤波、LMS滤波对该含噪声的正弦信号进行去噪声处理;
(6) 假设该信号是一个飞行器的某个方向的线位移信号,可否利用Kalman滤波对该信号进行滤波?
(7) 利用高阶谱理论对该信号进行谱估计和相应的AR模型、MA模型和ARMA模型估计;
(8) 利用小波变换(变换函数可以直接用Matlab里的函数)对该信号进行去噪声处理,并和前面的去噪声方法进行比较。
-Produce a random signal and two different frequency but frequency interval small sine signals, required to make the following analysis of two signals:
(1) for the random signal from the correlation coefficients, the autocorrelation function and draw the corresponding graphics
(2) make use of different parameters modeling method for out two random signal power spectrum
(3) using maximum likelihood estimation, recursive least square method and common parameters estimation method estimates that the model, including AR model, MA model and the parameters of the ARMA model we, order time decided oneself With Matlab toolbox and some of the modeling function is used compared the
(4) use trapped wave filter and MUSIC of the signal filter method of spectrum to estimate
(5) use Wiener filtering, LMS filtering on the sinusoidal signal with noise to noise treatment
(6) assumes the signal is a vehicle of a certain direction of displacement signal line, can you use Kalman filtering on the
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现代信号处理相关代码\signal .m
....................\《现代信号处理》作业.doc
现代信号处理相关代码
....................\《现代信号处理》作业.doc
现代信号处理相关代码