平稳随机过程非均匀采样信号的数字谱研究

目前的随机信号分析多是基于作等时间间隔采样以描述信号特征,而实际应用中往往不能避免采样的非均匀性;传统的谱分析方法必然产生原理误差。本文引入平稳随机点过程的知识,建立了广义平稳随机信号的非均匀采样序列模型,推导了其数字频谱的一般公式,分析了具有典型分布函数的随机采样情况。     

一类非均匀采样信号的数字谱

本文对实际采样过程中出现的一类非均匀、非理想采样信号进行了频谱分析,得到了此类信号的数字谱表达形式,并对非均匀采样周期信号的数字谱进行了深入的研究,得到了其数字谱完整的表达形式及其一些重要性质,最后,给出了该理论的一个应用一分析数字合成周期信号的频谱及信噪比．     

多路A/D非均匀采样信号的数字谱分析

对多路 A/D采样过程中出现的一类非均匀、非理想采样信号进行了频谱分析 ,得到了此类信号的数字谱表达形式 ,并对非均匀采样周期信号的数字谱进行了深入的研究 ,得到了其数字谱完整的表达形式及其一些重要性质。     

浅析非均匀采样信号的频谱和功率

在非均匀采样信号模型的基础上，给出了非均匀采样周期信号的频谱表达式，分析了非均匀采样信号平均功率和模拟信号功率谱的对应关系，并做了仿真以验证非均匀采样信号功率收敛于真值的条件(|fn-fn-1|≠1/MT整数倍)。     

非均匀采样信号的分数阶数字频谱研究

利用分数阶Fourier变换对实际采样过程中出现的非均匀、非理想采样信号进行了分数阶频谱分析与研究,得到了这类非均匀采样信号在分数阶Fourier变换域的数字频谱表达形式.由此进一步得到了非均匀采样Chirp信号在分数阶域的频谱表达式,并分析了非均匀采样Chirp信号在分数阶Fourier变换域的分数阶频谱性质,最后仿真结果证明了结论的正确性.     

基于随机采样法的光生混沌雷达信号周期性减弱研究

激光器产生混沌信号时,由于腔长原因使产生的信号具有周期性,若将该信号应用于雷达系统,易被识别与攻破。针对上述问题,文中提出采用随机采样法对原始混沌序列进行不等间隔随机采样,讨论分析了采样前后混沌的自相关和功率谱性能,结果显示采样后混沌信号自相关及功率谱的周期性均明显减弱并逐步消除。搭建基于光生混沌的调频雷达信号模型,理论推导分析了信号的频谱和模糊函数。结果表明,混沌自相关的旁瓣较原始混沌序列明显降低,功率谱也更加平坦,可获得sinc函数频谱和图钉状的模糊函数,该信号可以应用于雷达探测系统。     

一种非均匀采样信号重构方法

针对周期性非均匀采样信号,推导了其频谱和均匀采样序列频谱之间的关系,并基于此给出了一种信号重构方法。仿真结果验证了该方法的可行性。     

非均匀采样的频谱研究

非均匀采样的一个很大的优点就是它具有抗频率混叠的性能[],首先从均匀采样讨论由采样而引起的频谱混叠现象,在均匀采样和非均匀采样的频谱图对比中讨论两种采样方式引起的不同的频谱混叠现象,从对比中分析非均匀采样方式的优势。从最简单的非均匀采样方法逐步深入到完全随机的非均匀采样方法,研究由于采样方法的改变对数字信号频谱的影响。最后可以看到非均匀采样的方法可以将混叠信号的频谱降低到完全不影响对真实信号的检测。     

一种抗混叠的非均匀周期采样及其频谱分析方法

非均匀采样可以突破采样定理的限制,但非均匀采样信号的频谱在所有频率段均存在混叠频谱噪声,这些频谱噪声会淹没真实信号中的一些频谱幅度较小的信号,使得非均匀采样无法检测小信号。本文提出一种非均匀周期采样方法,该方法既具有非均匀采样抗混叠的特性,又具有均匀采样的消除混叠频谱噪声的特点。文中详细分析了非均匀采样产生混叠频谱噪声的原因,提出了基于非均匀周期采样的傅里叶变换方法,并给出了实验结果。理论和实验表明,非均匀周期采样方法是一种行之有效的采样方法。     

基于小波变换的非均匀采样信号频谱的研究

该文提出基于小波变换的非均匀采样信号频谱的检测方法,给出变换函数关系使得非均匀采样信号满足小波变换的两个基本条件。文中说明了小波的非均匀化过程,从均匀小波得到非均匀小波,以非均匀小波分析非均匀采样信号,得到非均匀采样信号的频谱。文中还说明了非均匀小波变换的抗混叠的原理以及对信号频谱的检测方法,最后给出实验结果。理论和实验表明,非均匀采样信号的小波变换方法是一种行之有效的非均匀采样信号的频率检测方法,使用该方法处理信号可以得到准确的频率估计效果。     

基于MATLAB GUI实现随机信号的分析与处理

以MATLAB GUI为软件开发平台,设计了随机信号的分析与处理系统.该系统以随机信号分析的基本理论和方法为基础,通过对随机信号进行相关性分析和功率谱密度分析,掌握有用信号和噪声信号的频谱特征,设计FIR数字滤波器滤除噪声信号,提取有用信号,完成对随机信号的有效去噪处理.该系统界面设计关观大方,功能设计简捷方便,并易于...     

Spectral Dispersion of Modulated Signals Due to Oscillator Phase Instability: White and Random Walk Phase Model

This paper considers the modeling of oscillator phase instability and the resulting spectral dispersion. A phase covariance matrix method is developed for determining the autocorrelation function and the power spectral density of the oscillator sinusoidal RF signal when corrupted by a superposition of a white phase random process and a random walk phase random process. By limiting the discussion to phase covariance matrices, it is shown that the direct use of a certain class of nonstationary phase random processes leads to stationary RF signal autocorrelation functions and associated power spectral densities. This is so despite the nonstationary phase driving force. The procedures provided here are also applied towards the determination of the average autocorrelation function and the average spectrum when the cisoidal oscillator signal undergoes modulation. Modulating waveforms used as examples include the CW, the infinite pulse train, and the finite pulse train.     

随机噪声信号的功率谱估计的仿真

在通讯与电子信息工程行业及领域中,大部分问题的解决需要进行估计一个随机信号在频率域上的功率谱分布,诸如此类的问题有很多,比如：设计滤波器消除噪声信号,振动随机信号的回波抵消,随机信号的特征抽取与表示等等。功率谱估计的分类：一般分为两大类,一类是参数法功率谱估计,一类是非参数法功率谱估计。参数法功率谱估计通常对数据进行一种建模,比如把数据建模成滑动平均模型（Moving Average）,或者自回归（Autoregressive）模型,而非参数法功率谱估计。除了要求信号满足广义平稳之外,不需要其它的统计假设。与非参数法相比较,参数法的优点是在一个给定的数据集合上能够有较少的误差、偏差与方差。     

An adaptive digital predistortion for compensating nonlinear distortions in RF power amplifier with memory effects

In this paper, an adaptive digital predistortion based on a memory polynomial model is proposed in order to linearize the power amplifier with memory effect. The coefficients of the power amplifier model have been extracted using a least square method and those of predistortion have been identified by applying an indirect learning structure. Finally, the performance of digital predistortion has been demonstrated using the simulation of the power amplifier and the digital predistortion excited by a modulated 16 QAM signal in Matlab software. According to the simulation results, the criterion of adjacent channel power ratio (ACPR) declined by around 15 dB and the input/output power spectrum density of the power amplifier has quite similar curves. The linearized power amplifier output spectrum demonstrates the superiority of the proposed predistorter in eliminating the spectral regrowth which is caused by the memory effect in comparison to the other linearization methods.     

Common Pitfalls in the Application of Stationary Process Theory to Time-Sampled and Modulated Signals

The common practice of applying the theory of stationary stochastic processes to a cyclostationary process by introducing random phase(s) into the probabilistic model in order to stationarize the process can lead to erroneous results, such as incorrect formulas for power spectral density. This is illustrated by showing that commonly used formulas for signals that have undergone frequency conversion or time sampling can be incorrect. The source of error is shown to be inappropriate phase-randomization procedures. The correct procedure is described, and corrected formulas are given. The problem is further illustrated by showing that commonly used resolution and reliability (mean and variance) formulas for spectrum analyzers must be corrected for cyclostationary signals. It is explained that all corrections to formulas reflect the effects of spectral correlation. These effects are inappropriately averaged out by inappropriate phase-randomization procedures. It is further explained that these inappropriate procedures destroy the important property of ergodicity of the probabilistic model.     

Unified study on characteristic spectrum representation of signals and spectral decomposition for stationary random signals

The unified relationship between the signal characteristic spectrum representation and the spectral decomposition for the stationary random signal was deeply studied. By using the relations among the differential operators, the integral operator and the Green's function of the characteristic differential equation, the inverse relationship between the Hermitian differential operator and the Hermitian integral operator were given, the characteristic differential equation and corresponding characteristic integral equation were demonstrated, and the spectral representations of both Hermitian differential and integral operators and the general spectral representations for both operators were provided. Based on the superposition method of the stochastic simple harmonic vibration and the Hilbert space unitary operator method for the stationary random signal spectral decomposition, the connection and unification on mathematics of the signal characteristic spectral representation and the stationary random signal spectral decomposition are revealed.     

Spectral properties of signal fading and Doppler spectra distribution in urban mobile communication links

In this work, we continue the analysis of a probabilistic approach and the corresponding stochastic multi‐parametric model of wave propagation, in built‐up areas with randomly distributed buildings. We have concentrated on the spectral properties of signal strength spatial variations and on Doppler spread spectrum distribution of signal power. The analysis is based on a unified stochastic approach of radio wave propagation above the built‐up terrain with applications to mobile communications. We analyze the signal power spectrum of spatial frequencies and the signal power distribution in the Doppler domain for moving vehicles, taking into account a Doppler shift proportional to the vehicle antenna speed relative to the base station. The comparison between the theoretical prediction and experimental data was motivated by the proposed stochastic model and other existing statistical models to verify the signal power distribution in the Doppler domain for various urban environments and terminal heights with respect to building rooftops. New effects of terrain features on signal spectrum are obtained, examined and compared with existing models. Copyright © 2006 John Wiley & Sons, Ltd.