非均匀采样下小信号检测与重构的陷波方法

非均匀采样由于其具有不受采样频率限制和抗混叠等优点,使得其应用十分广泛.但非均匀采样会引起频谱噪声,使得信号中的幅度小的频率成分不易检出.本文分析了非均匀采样引起频谱噪声的原因,根据非均匀采样检测得到的大幅度信号,应用陷波器将其消除,降低频谱噪声,从而检测和重构出小信号.文中详细说明了陷波方法的原理以及陷波器在非均匀采样下的应用,最后给出实验结果.理论和实验表明,采用陷波方法可以检测和重构出非均匀采样下的小信号.     

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。     

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。     

基于分数阶Fourier变换的一种新的非均匀采样方法

由于采样设备和被采样信号的限制,完全均匀采样是无法实现的,因此不得不进行非均匀采样.根据信号的时变特征,在信号的低频部分,用较低的频率对信号进行采样;在信号的高频部分,用较高的频率对信号进行采样.在此基础上,应用分数阶Fourier变换对非均匀采样Chirp信号进行分析,得到非均匀采样Chirp信号在分数阶Fourier变换域的频谱表达式,并分析其在分数阶域的频谱性质.该方法解决了整个采样时段内由于过采样造成的数据冗余性,满足了实时性要求,且具有较好的抗噪声能力.这里提出的非均匀采样方法满足了采样定理的要求,并得到了均匀采信号在分数阶域的频谱表达式.     

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。     

直扩系统中IIR格型滤波器抑制窄带干扰新方法与性能分析

该文提出一种直扩系统中基于IIR格型陷波器时域频域相结合的窄带干扰抑制新方法,该方法采用时域频域并行处理的结构,通过最优加窗和频谱校正技术在频域精确估计出干扰信号的频率,根据频率估计误差和干信比求出最优陷波带宽,由此自适应地调整时域IIR格型陷波器的参数。分析和实验表明,该方法较传统的自适应IIR窄带干扰抑制方法不仅具有更高的稳定性和实时性,而且在干扰得到有效抑制的情况下使有用信号的损失达到最小。     

分数阶Fourier域非均匀采样LFM信号的检测概率

为了减少分数阶Fourier域线性调频(LFM)信号处理的计算量,满足实时性要求,提出了一种新的非均匀采样方法.与传统的非均匀采样方法相比,该方法既解决了整个采样时段内由于过采样造成的数据冗余性,也消除了信号时变特征造成的局部冗余性.最后,应用该方法得到了非均匀采样LFM信号在分数阶Fourier变换域的频谱表达式,并对不同信噪比(SNR)条件下信号的检测概率进行了研究.结果表明,该方法具有较好的抗噪声能力.     

分数阶Fourier域多分量微弱LFM信号的检测

为了提高对多分量微弱LFM信号的检测能力,提出了一种基于分数阶Fourier域非均匀采样理论的信号检测方法。首先提出了一种自适应非均匀采样方法,建立了该方法的模型,得到了非均匀采样LFM信号在分数阶Fourier域的频谱表达式。在此基础上,对多分量非均匀采样LFM信号进行了检测研究。计算机仿真结果表明,同传统的信号检测方法相比,该方法对微弱LFM信号具有较好的检测能力,而且减少了运算量,满足了实时性要求。     

基于双稳类随机共振的信息检测

研究了小参数随机共振(SR)的响应幅值与信号频率和噪声强度的关系,并从噪声频谱的罗伦兹(Lorentz) 分布特性推出,只有在噪声能量集中的低频区域才能产生随机共振的论点。得出了二次采样大参数类随机共振的实现条件,即采样频率至少是信号频率的50倍并根据噪声强度选择二次采样频率。在大参数情况下,由双稳系统输入输出信噪比的分析,阐明了大参数类随机共振方法从强噪声中检测出弱信号的可行性。运用周期和非周期弱信号的检测实例,进一步证明了该方法的有效性和实用性。     

分数阶Fourier域上非均匀采样信号的频谱重构研究

 本文研究了分数阶Fourier变换域上非均匀采样信号的重构问题.首先得到周期非均匀采样信号经非均匀分数阶Fourier变换后的频谱表达式,研究了该分数阶频谱和信号连续分数阶频谱之间的关系,并基于该关系式提出了一种分数阶Fourier域周期非均匀采样信号的频谱重构算法;其次,讨论了分数阶Fourier变换域上更加一般情况下非均匀采样信号重构问题;最后,给出了周期非均匀采样信号频谱重构的仿真结果.     

Signal Acquisition and Processing Method for Capacitive Electromagnetic Flowmeter

A kind of signal acquisition circuit and the related signal processing method of the capacitance electromagnetic flowmeter (EMF) were introduced. The circuit can eliminate the influence of distributed capacitance on the input impedance of the operational amplifier, and greatly improve the input impedance of the detection circuit to overcome the disadvantage of high signal source impedance. The rotating capacitor filter is a signal processing method based on the phase-sensitive detection technology. It can extract the weak signal from the strong and wide-band background noise, so it is very suitable for the processing of capacitive electromagnetic flow signals. Through the comparison of the signal amplitude obtained at different flow rates and the comparison of signal spectrum components before and after the filter, the effectiveness of the bootstrap signal acquisition circuit and rotating capacitor filtering method is verified.     

一种信号检测预处理的改进多尺度形态学滤波方法

通信信道冲击噪声干扰和宽带接收机非线性影响会导致接收的信号噪声基底不平坦,若不考虑噪声基底起伏而直接进行检测信号,可能会导致漏检。为提高信号检测精度,需估计信号噪声基底。为解决该问题,提出了一种信号检测预处理的改进多尺度形态学滤波方法。该方法采用多尺度开-闭组合运算对信号频谱进行形态学滤波,利用不同尺度的结构元素对不同带宽的信号进行局部滤波,并利用滤波后凸包的存在性作为滤波终止条件。仿真结果表明,该方法可以有效将不同带宽的信号滤除,有效提取噪声基底噪声,同时可消除频谱中低电平毛刺对噪声估计的影响。     

A constrained notch Fourier transform

The paper presents a new sliding algorithm for estimating the amplitude and phase of the Fourier coefficients of noise corrupted harmonic signals given a priori knowledge of the signal frequencies. The proposed method is similar in principle to the notch Fourier transform (NFT) technique suggested by Tadokoro et al. [1987] except that it employs an infinite impulse response (IIR) rather than a finite impulse response (FIR) notch filter parameterization. This modification provides bandwidth controlled bandpass (BP) filters whose center frequencies are equally spaced in the frequency spectrum. In this sense, the proposed technique can be regarded as a constrained notch Fourier transform (CNFT). Sliding algorithms have been derived for both the NFT and CNFT for the purpose of estimating the Fourier coefficients of the sinusoidal components. The paper also proposes a similar algorithm to the CNFT for the signals containing sinusoids at arbitrary known frequencies. The main feature of the modified CNFT is that it uses second-order IIR BP filters whose bandwidth and center frequency can be adjusted independently. The bandwidth control aspect provides the user with an efficient means of achieving the required resolution as well as reducing spectral leakage. In general, the proposed approach leads to considerable reduction in terms of computational burden and memory storage     

Neural-network-based adaptive matched filtering for QRS detection

We have developed an adaptive matched filtering algorithm based upon an artificial neural network (ANN) for QRS detection. We use an ANN adaptive whitening filter to model the lower frequencies of the ECG which are inherently nonlinear and nonstationary. The residual signal which contains mostly higher frequency QRS complex energy is then passed through a linear matched filter to detect the location of the QRS complex. We developed an algorithm to adaptively update the matched filter template from the detected QRS complex in the ECG signal itself so that the template can be customized to an individual subject. This ANN whitening filter is very effective at removing the time-varying, nonlinear noise characteristic of ECG signals. Using this novel approach, the detection rate for a very noisy patient record in the MIT/BIH arrhythmia database is 99.5%, which compares favorably to the 97.5% obtained using a linear adaptive whitening filter and the 96.5% achieved with a bandpass filtering method.     

一种抑制参数估计背景色噪声的形态学滤波算法

根据循环平稳有关理论,通信信号的非线性变换频谱中存在体现信号各阶循环平稳性的离散谱线,谱线位置对应着信号载波频率和符号速率的线性组合。通过提取这些离散谱线可以完成信号的基本参数估计,然而非线性变换不仅能产生对应于信号载波频率、符号速率等参数的正弦分量,而且还会形成不利于谱线提取的背景色噪声,当信号信噪比低、数据量小时,色噪声对谱线提取的影响尤为突出。针对这一问题,本文深入研究了信号非线性变换谱特征,结合数学形态学基本理论,提出了一种基于离散灰度形态滤波的方法来抑制背景色噪声。首先利用开运算估计背景色噪声,其次运用顶帽变换进行白化处理,通过闭运算填平杂散负脉冲,增强谱线相对强度,最后设置检测门限,提取离散谱线,完成信号对应参数的估计。详尽的Monte Carlo仿真给出了谱线检测性能与信号脉冲成型系数、信噪比和数据量之间的关系。结果表明,该算法有效抑制了背景色噪声,在信噪比低、脉冲成型系数小、数据量少的情况下提高了谱线检测性能,从而证实了该算法的有效性。      

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。     

一种简易数字控制微弱信号检测装置的设计

设计了基于STM32F407数字控制的微弱信号检测装置,整个系统包括信号预处理模块、参考信号源模块、互相关检测模块、ADC采样模块及STM32处理器控制模块五个主要部分。前级通过加法器把微弱待测信号和强噪声信号相加,模拟出信号淹没在强噪声中的微弱信号源;通过带通滤波、程控放大进行预处理后的信号在AD630中进行互相关检测处理;所得信号经过低通滤波后送入ADS1271采样分析,最后得出待测微弱信号的幅度、相位和频率等信息。在强噪声源幅度为2Vpp的情况下,实现了对20mVpp微弱信号的检测。     

On‐line wavelet filtering of narrowband noise in signal detection of spread spectrum system for location tracking

Spread spectrum signal transmitted by wireless channel for location tracking can be severely corrupted by noise due to external disturbances. Narrowband noise is the most effective interference that can make measurement signal undetected. However, the current methods for narrowband interference (NBI) suppression are either very time‐consuming or add distortion to the signal received. In this paper, an adaptive Gaussian wavelet filter with optimal time–frequency localization and variable notch depth is proposed to suppress a large number of NBIs with additive white Gaussian noise and pulsed noise that interfere with the spread spectrum communication system. The filtering of both continuous and time‐varying NBIs with fast resampling is performed in conjunction with the fast Fourier transform‐based correlation for peak detection, and is computationally efficient for real‐time operation of signal detection. The performance of the adaptive filter has been evaluated by experiments employing a reliable noise detector. Experimental results demonstrate that the proposed wavelet filter isolates the signals from the NBI in accordance with the corrupted frequency contents while preserving the desired spread spectrum signal, and improves signal to noise ratio for peak detection leading to higher accuracy of timing measurement for real‐time wireless location. Copyright © 2011 John Wiley & Sons, Ltd.     

心电信号处理中的数字滤波器的设计

心电信号是一种基本的人体信号,其中常带有肌电干扰、基线漂移和工频干扰等各种噪声,为了得到不失真的原始心电信号,在显示信号前要进行必要的滤波预处理.介绍了处理ECG信号中滤波器的设计,包括去除噪声的低通、高通和带阻滤波器.