无线电信号功率谱的分析与仿真

随着无线电行业的迅速发展,无线电信号的频谱分析应用得到广泛应用。然而在现代信号处理中,谱估计法可分为经典功率谱估计法和现代功率谱估计法。经典功率谱估计法作为一种分辨率较低的谱估计方法。现代谱估计法利用参数化模型来进行谱估计,大大地提高了功率谱估计的分辨率。文中使用不同的谱估计方法求功率谱估计值,得到各种已调信号在高斯白噪声信道传输下的功率谱估计图,含AM、DSB、SSB、FM、PM、2ASK、2FSK和2PSK等8种信号。     

基于AR模型的载频测量

从现代谱估计出发,利用Yule_Walker方程构造信号的AR模型,从中提取信号的调制载波频率信息。信号的功率谱表示了信号能量的分布。用谱估计法估计出载波频率后,依据频谱对所测频率进行修正,可有效提高载波频率的测量精度。     

基于CZT和Zoom-FFT的频谱细化分析中能量泄漏的研究

在经典谱估计理论中,频谱能量泄漏是导致频谱失真的主要误差因素。CZT和Zoom-FFT是两种重要的频谱细化分析算法,本文对这两种方法的能量泄漏进行了详细的分析,并仿真分析了不同种类和不同宽度的窗对谱泄漏的影响。分析和仿真的结果表明,Zoom-FFT可以大大减小频谱能量泄漏,获得更高质量的细化频谱。     

基于CZT和Zoom-FFT的频谱细化分析中能量泄漏的研究

在经典谱估计理论中，频谱能量泄漏是导致频谱失真的主要误差因素。CZT和Zoom-FFT是两种重要的频谱细化分析算法，本文对这两种方法的能量泄漏进行了详细的分析，并仿真分析了不同种类和不同宽度的窗对谱泄漏的影响。分析和仿真的结果表明，Zoom-FFT可以大大减小频谱能量泄漏，获得更高质量的细化频谱。     

基于二维幅值谱的正弦信号频率估计

信号中含有噪声或非整周期截断时发生的频谱泄漏是导致正弦信号频率估计精度不高的主要原因。针对这一问题,从扩展信号频谱表征方式出发,将经典幅值谱扩展至不受频谱泄漏制约、表现力更强、可读性更好的二维幅值谱。与经典幅值谱相比,二维幅值谱除包含信号的频率个数、幅值信息外还包含了易于获取的周期信息,且具有一定的抗噪性,在信噪比低至-10dB时仍有较好表现力。提出一种估计方法,先从二维幅值谱中估计出信号的周期T,然后根据信号采样频率、信号频率、以及信号周期T之间的定量关系完成信号的频率估计。实验结果证明了该方法的有效性。基于二雏幅值谱的正弦信号频率估计方法为正弦信号的频谱估计提供了一种新思路。     

频谱细化及频谱校正技术在激光多普勒测速仪中的应用

提出了对多普勒信号先进行频谱细化,再进行频谱校正的方法,阐述了几种常见的离散频谱细化和频谱校正算法的基本原理,并运用它们对不同频率的理想正弦信号和实测的多普勒信号进行谱仿真和实测研究。理论分析和实验结果表明:频谱细化算法中Goertzel细化算法所需的运算量最少,计算速度最快;频谱校正算法中比值校正算法校正公式简单,运算量少,且校正精度较高;频谱细化和频谱校正技术大大提高了频谱分辨率,将其运用于频谱分析型激光多普勒测速仪中切实可行。     

基于JEM效应的一种雷达目标识别方法

根据ＪＥＭ（喷气发动机调制）效应和雷达目标回波谱经特点,本文提出了一种雷达目标识别方法。此方法是利用小波变换,得到雷达目标回波信号的频率调频指数,对信号进行相位补偿,然后滤掉信号中的机身频谱和多普勒频谱,再进行相位补偿,用ＦＦＴ（快速傅立叶变换）就可以得到目标的调制谱,由此实现目标识别。由实测数据进行分机处理,证明利用目标的调制谱识别目标,认识效果很好。     

连续信号频谱分析及误差研究

实际中持续时间无限长信号或非带限信号,需要对信号进行预处理。由预处理和离散傅里叶变换DFT近似拟合得到连续信号频谱,会产生频谱混叠、栅栏效应和截断效应。本文分析了DFT进行连续信号频谱分析的原理和步骤,分析了上述过程产生误差的原理和相应的解决对策,用理论和实例论证了如何合理选择分析参数,平衡频率分辨率和误差效应。     

网络无线侦察接收机软件系统的设计和实现

为兼容无线电监测网传输协议(RMTP),实时监测无线电信号并测量其频率、带宽等技术参数,无线侦察接收机软件系统通过网络接收硬件采集的无线电信号,使用经典功率谱估计方法对其进行频谱分析.系统用户界面采用跨平台GUI库CodeBlocks+wxWidgets进行开发,使用二维图形库wxMathPlot开发画图模块,将测量和分析的结果以频谱图的方式展现给用户.分析了系统的基本架构,wxWidgets库的使用方法,经典功率谱估计算法,重点讨论了如何解决网络传输过程中可能遇到的"粘包"问题.     

低频电磁信号的频率细化技术

在低频电磁信号中,为了得到某一个窄带内频谱的精细结构,提高频谱的频率分辨率,满足良好的实时性要求,文中提出了基于快速傅里叶变换算法的频率细化技术。通过复调制频率细化,将某一局部频谱放大,以便对低频电磁信号的频谱进行快速、准确的分析。利用复调制频率细化方法,对低频电磁信号进行Matlab仿真,其仿真结果表明,该方法比线性...     

Power spectral density of unevenly sampled data by least-squareanalysis: performance and application to heart rate signals

This work studies the frequency behavior of a least-square method to estimate the power spectral density of unevenly sampled signals. When the uneven sampling can be modeled as uniform sampling plus a stationary random deviation, this spectrum results in a periodic repetition of the original continuous time spectrum at the mean Nyquist frequency, with a low-pass effect affecting upper frequency bands that depends on the sampling dispersion. If the dispersion is small compared with the mean sampling period, the estimation at the base band is unbiased with practically no dispersion. When uneven sampling is modeled by a deterministic sinusoidal variation respect to the uniform sampling the obtained results are in agreement with those obtained for small random deviation. This approximation is usually well satisfied in signals like heart rate (HR) series. The theoretically predicted performance has been tested and corroborated with simulated and real HR signals. The Lomb method has been compared with the classical power spectral density (PSD) estimators that include resampling to get uniform sampling. The authors have found that the Lomb method avoids the major problem of classical methods: the low-pass effect of the resampling. Also only frequencies up to the mean Nyquist frequency should be considered (lower than 0.5 Hz if the HR is lower than 60 bpm). It is concluded that for PSD estimation of unevenly sampled signals the Lomb method is more suitable than fast Fourier transform or autoregressive estimate with linear or cubic interpolation. In extreme situations (low-HR or high-frequency components) the Lomb estimate still introduces high-frequency contamination that suggest further studies of superior performance interpolators. In the case of HR signals the authors have also marked the convenience of selecting a stationary heart rate period to carry out a heart rate variability analysis     

Data-adaptive evolutionary spectral estimation

We present a novel data-adaptive estimator for the evolutionary spectrum of nonstationary signals. We model the signal at a frequency of interest as a sinusoid with a time-varying amplitude, which is accurately represented by an orthonormal basis expansion. We then compute a minimum mean-squared error estimate of this amplitude and use it to estimate the time-varying spectrum at that frequency, all while minimizing the interference from the signal components at other frequencies. Repeating the process over all frequencies, we obtain a power distribution that is consistent with the Wold-Cramer evolutionary spectrum and reduces to Capon's (1969) method for the stationary case. Our estimator possesses desirable properties in terms of time-frequency resolution and positivity and is robust in the spectral estimation of noisy nonstationary data. We also propose a new estimator for the autocorrelation of nonstationary signals. This autocorrelation estimate is needed in the data-adaptive spectral estimation. We illustrate the performance of our estimator using simulation examples and compare it with the recently presented evolutionary periodogram and the bilinear time-frequency distribution with exponential kernels     

Joint angular and spectral estimation technique using nonlinear Kalman filters for cognitive radio

The joint estimation of direction of arrivals (DOA) and carrier frequencies of band-limited source signals is considered in this paper. A novel technique based on nonlinear Kalman filters is proposed for this joint angular and spectral estimation problem for cognitive radio (CR). Since sampling a wideband spectrum at Nyquist rate increases the analog-to-digital converter (ADC) requirements, we propose executing Kalman filter algorithm over a spatial state space model. Thus, one time sample is required and hardware complexity is reduced. Two types of nonlinear Kalman filters, extended Kalman filter (EKF) and unscented Kalman filter (UKF), are proposed. We consider their sub-optimal performance and show how to control their convergence. However, the proposed algorithms can detect a number of source signals limited to the number of elements in employing arrays.     

硬边光阑对多色矢量光束光谱开关的影响

从偏振矩阵和交叉谱密度传输公式出发,分析了硬边光阑对多色矢量高斯-谢尔模型光束轴上光谱开关的影响。结果表明,插入偏振片前,光谱开关的光谱跃迁量Δ随截断参数δ的增加而减少,光谱极小值Smin和位置zc/z0随δ的增加而增大。插入偏振片后,光谱开关随δ的变化规律与插入偏振片前的变化规律相同。但偏振片的旋转角度θ会对出现光谱开关的δ的范围产生影响。     

Improved spectral resolution

Two new methods are presented for the estimation of the frequencies of closely spaced complex valued sinusoidal signals in the presence of noise. The most effective method is a computationally efficient method for realization of maximum likelihood or maximum posterior probability estimates of the frequencies. The second method is a class of algorithms for removing some of the deficiencies of present adaptive filtering and correlation-estimation approaches to estimation of frequencies, such as the forward-backward linear prediction method. In both of these new methods one is fitting a signal model to data. In method 1 the data are the observed samples of two complex sinusoids plus noise. In the second method the data are elements of an estimated correlation matrix, or of some of its eigenvectors, obtained from the observed samples.     

Digital implementations of spectral correlation analyzers

The issues involved in the design of computationally efficient algorithms for spectral correlation estimation and the resulting impact on high-speed digital realization are addressed. The spectral correlation analyzer is characterized as a periodically time-varying quadratic system with a kernel possessing certain gross properties. The mean and variance of the output are expressed in terms of the kernel and the spectral correlation function of the input. Three realizations are analyzed in detail. One is based on the frequency-smoothing method of cross spectral analysis. The others are variants of the time-smoothing method. For each of these realizations, an exact expression for the quadratic system kernel is given, the digital implementation is developed, and a detailed complexity analysis is presented. High-speed pipeline realizations of the algorithms are analyzed, and related special issues are discussed. Examples involving the calculation of the spectral correlation function in near-real-time for broadband communications signals are discussed     

基于短时谱分析的语音增强改进算法

介绍了基本的语音增强短时谱分析算法的原理.基于经典的谱减算法进行改进,引入了先验信噪比估计,以及时间回归平均法对噪声进行更新,以及一系列后处理改进方法,提高了降噪效果.在保持语音可懂度的基础上,增强了噪声衰减量.主、客观实验结果表明,针对汽车噪声一类的平稳噪声,算法效果出色;而对于街道、人声一类非稳态噪声,该算法性能优...     

Noninvasive estimation of tissue temperature via high-resolution spectral analysis techniques

We address the noninvasive temperature estimation from pulse-echo radio frequency signals from standard diagnostic ultrasound imaging equipment. In particular, we investigate the use of a high-resolution spectral estimation method for tracking frequency shifts at two or more harmonic frequencies associated with temperature change. The new approach, employing generalized second-order statistics, is shown to produce superior frequency shift estimates when compared to conventional high-resolution spectral estimation methods Seip and Ebbini (1995). Furthermore, temperature estimates from the new algorithm are compared with results from the more commonly used echo shift method described in Simon et al. (1998).     

Zero-crossing based spectral analysis and SVD spectral analysis forformant frequency estimation in noise

The authors discuss a method for spectral analysis of noise corrupted signals using statistical properties of the zero-crossing intervals. It is shown that an initial stage of filter-bank analysis is effective for achieving noise robustness. The technique is compared with currently popular spectral analysis techniques based on singular value decomposition and is found to provide generally better resolution and lower variance at low signal to noise ratios (SNRs). These techniques, along with three established methods and three variations of these method, are further evaluated for their effectiveness for formant frequency estimation of noise corrupted speech. The theoretical results predict and experimental results confirm that the zero-crossing method performs well for estimating low frequencies and hence for first formant frequency estimation in speech at high noise levels (~0 dB SNR). Otherwise, J.A. Cadzow's high performance method (1983) is found to be a close alternative for reliable spectral estimation. As expected the overall performance of all techniques is found to degrade for speech data. The standard autocorrelation-LPC method is found best for clean speech and all methods deteriorate roughly equally in noise     

Estimation of M-wave scale factor during sustained contractions at high stimulation rate

In this paper, we propose a time-domain index to assess M-wave widening during high-frequency stimulation, as an objective parameter for quantifying muscle fatigue. At high stimulation frequencies, signal truncation, due to the delivery of the electrical stimulus before the M-wave generated by the previous stimulus extinguishes, biases the spectral frequency variables usually computed to estimate M-wave widening. Thus, we propose an estimator of the scale factor between two truncated M-waves. The estimator is derived from the Scale Transforms of the two signals, with an efficient implementation that avoids limits of resolution. The method was tested on both simulated and experimental signals. The simulations showed that the proposed technique is significantly less affected by signal truncation than previous approaches. The experimental recordings were collected from 11 subjects at stimulation frequencies of 20, 40, and 60 Hz. The scale factor estimation assessed M-wave widening in the three conditions, differentiating between the different rates of change of signal widening. The method proved to be significantly superior to M-wave spectral analysis. The technique can be applied to investigate myoelectric manifestations of muscle fatigue at stimulation rates that could not be analyzed in the past and, thus, opens new perspectives in the evaluation of electrical stimulation for training and rehabilitation protocols.