噪声中信号时变参数的最佳解调

本文研究了在噪声中信号时变参数的非线性估计,提出了瞬时最大似然估计的概念,证明了这个瞬时最大似然估计是渐近充份的,给出了最佳估计的新途径;其实质是把信号时变参数非线性估计这样一个难题,分解为求出瞬时最大似然估计和对瞬时最大似然估计的最佳处理。文中定义实现瞬时最大似然估计的装置为最佳差调器,给出了它的一般构造。 附录比较了本文和D.C.Youla及D.L.Snyder等文的结果。     

基于FFT滑动平均极大似然法的正弦信号频率估计

该文基于正弦信号采样序列的FFT频谱,利用谱图上多条显著谱线与峰值谱线实部之间的关系,推导建立了一用于信号频率估计的滑动平均模型,基于此模型得出的极大似然频率估计器结合传统的Quinn方法后得到一种新的基于FFT谱滑动平均极大似然估计算法。仿真实验表明该算法精确有效,估计性能优于Rife,Quinn法,十分接近CRLB下限,计算量不大且信噪比门限要求可降至-9dB左右。     

基于柯西分布的跳频信号参数最大似然估计方法

该文针对传统的跳频信号参数估计方法在alpha稳定分布噪声下性能严重退化的问题,引入基于柯西分布的最大似然估计方法。将跳频信号分解到由信号包络参数和频率参数构成的2维平面,基于柯西分布建立最大似然函数,在抑制alpha稳定分布噪声的同时,直接对信号的频率参数进行估计。在构建的最大似然函数基础上,该方法依据跳频信号的短时平稳性,对信号进行加窗,有效获得信号的跳频频率及其跳变次序,进而实现对信号的跳变时刻和跳频周期等参数的估计。仿真结果表明,在alpha稳定分布噪声环境中,相比基于分数低阶统计量及基于Myriad滤波的时频分析方法,该文所提方法提高了跳频信号的参数估计精度,具有良好的稳健性。     

一种LFM信号最大似然估计模型与参数估计快速算法

从最大似然估计模型入手,提出了一种适合在一般高斯噪声环境（包括色噪声）下LFM信号目标的参数估计模型和基于此估计模型的调频斜率和初始频率估计的快速算法。此方法获得了最大似然方法估计精度高的优点,且运算量比传统的最大似然方法大大降低。另外推导出了一般高斯环境下的LFM参数估计的CRB界,为一般高斯环境下的估计的参数的方差提供实际下界。     

LFM信号参数估计的最大似然改进算法

为实现含噪声LFM信号参数的快速检测和精确估计,提出了一种基于延时相关解线调的最大似然估计改进算法,即首先在时域内进行延时相关解线调,然后对解线调后含噪声信号进行经典功率谱估计,得到调频斜率的粗略估计,将此估计值作为初始值,再进行最大似然估计,得到调频斜率的精确估计值,用此精确估计值对原LFM信号进行解线调,再以同样的思路可以得到LFM信号初始频率的最大似然精确估计值。仿真实验证明了该算法的有效性。     

双通道接收的相位差估算方法

分析了双通道接收相位差的时域、频域计算方法，基于DFT的频域估算方法，充分利用了DFT、对信噪比的改善作用，克服了时域方法要求较高信噪比的缺点，能有效地抑制噪声，提高测量精度。但是离散的DFT输出谱限制了信噪比的改善，文中利用截断DFT的输出谱特性来估计信号频率，再用一次最大似然估计得到信号相位估计，减小运算量。分析和仿真表明文中方法可以得到接近最大似然法的测量精度。     

基于MEMS矢量传声器的声源DOA估计

微机电系统（Micro Electro Mechanical System,MEMS）矢量传声器在声源波达方向（Direction of Arrival,DOA）估计领域具有巨大的潜力及优势。本文分析MEMS矢量传声器接收信号模型,并结合随机性最大似然原理推导矢量传声器DOA估计模型。针对时变非均匀噪声影响矢量传声器DOA估计性能的问题,提出基于空间噪声预白化的极大似然DOA估计方法。此外,开展仿真分析及园区环境下的定向验证试验。结果表明,通过噪声协方差估计矩阵对矢量传声器接收信号数据进行预白化处理,能够克服时变非均匀噪声对信号协方差矩阵的影响,实现矢量传声器对目标DOA精确估计,验证了该方法的有效性及实用性。     

分形差分高斯噪声中正弦波频率估计

本文提出了分形噪声中的谐波恢复问题，针对分形差分高斯噪声中正弦波频率估计，提出了一种偏差补偿线性最小二乘与极大似然估计相结合的组合算法。模拟实验结果表明，该方法不仅具有高的分辨率，而且能有效地抑制分形差分高斯噪声的影响。     

微多普勒瞬时频率估计算法对噪声的适应性能研究

微多普勒特征提取的关键在于瞬时频率的计算,峰值检测法和一阶时间条件矩法是基于高分辨时频分布的两种瞬时频率估计算法.本文对两种瞬时频率估计算法对噪声的适应性能进行了理论分析和仿真计算,结果表明,当信号受噪声污染后,在一定的信噪比条件下,峰值检测法瞬时频率估计算法对能量分布的变化不敏感,但一阶时间条件矩法瞬时频率估计算法对时频域能量分布十分敏感,因此,峰值检测法较一阶时间条件矩法对噪声具有鲁棒性.实际中雷达接收到的信号都是受噪声污染的,分析两种算法对噪声的适应性能对工程实际应用具有重要参考价值.     

复高斯白噪声下基于多种方法的频率估计

对常用的几种频率估计方法进行了回顾,简单介绍了其中两种频率估计方法的原理;并应用最大似然、协方差、MUSIC、ESPRIT这几种方法对复高斯白噪声下信号的频率进行估计。给出了计算机仿真结果,并对各种方法估计结果进行了比较。比较结果表明:基于旋转不变的互谱估计方法(ESPRIT)避免了在固有的整个频域搜索,且能有效抑制噪声,具有良好频率估计性能。     

A high-resolution quadratic time-frequency distribution formulticomponent signals analysis

The paper introduces a new kernel for the design of a high resolution time-frequency distribution (TFD). We show that this distribution can solve problems that the Wigner-Ville distribution (WVD) or the spectrogram cannot. In particular, the proposed distribution can resolve two close signals in the time-frequency domain that the two other distributions cannot. Moreover, we show that the proposed distribution is more accurate than the WVD and the spectrogram in the estimation of the instantaneous frequency of a stepped FM signal embedded in additive Gaussian noise. Synthetic and real data collected from real-world applications are shown to validate the proposed distribution     

An Improved SAR Image Denoising Metho d Based on Bo otstrap Statistical Estimation with ICA Basis

A new method for Synthetic aperture radar (SAR) image denoising is proposed. The prior information of speckle statistical model can be exploited to judge its distribution. The basis of SAR image can be estimated by Independent component analysis (ICA), and these bases can be divided into two different subspaces (noise and real signal subspaces) through a linear classifier. Then para-metric Bootstrap estimates the parameters of speckle sta-tistical model on the noise signal subspace, and the non-parametric Bootstrap can estimate the distribution of real image on the real signal subspace. According to different results estimated by Bootstrap, corresponding Maximum a posterior probability (MAP) filter will be selected for im-age denoising, using the noise model’s parameter for adap-tive filtering. Experiments show that the image processed by this new method can achieve a better visual perception and ob jective evaluation results.     

一种集成SVD和PWVD的VCO非线性度检测新方法

针对现有压控振荡器(VCO)非线性度检测方法可靠性和普适性较差的问题,提出一种集成奇异值分解(SVD)降噪和伪魏格纳威尔分布(PWVD)瞬时频率估计的VCO非线性度检测新方法。该方法首先对待检测对象中频信号进行降噪处理,然后将降噪后的中频信号通过希尔伯特(Hilbert)变换转换为解析信号,最后利用自适应窗长的PWVD瞬时频率估计算法处理解析信号,实现中频信号的瞬时频率估计。仿真结果表明了文中方法的有效性、较强的环境适应能力和普适性。     

Classification of nonstationary narrowband signals using segmented chirp features and hidden Gauss-Markov models

A method is provided for classifying finite-duration signals with narrow instantaneous bandwidth and dynamic instantaneous frequency (IF). In this method, events are partitioned into nonoverlapping segments, and each segment is modeled as a linear chirp, forming a piecewise-linear IF model. The start frequency, chirp rate, signal energy, and noise energy are estimated in each segment. The resulting sequences of frequency and rate features for each event are classified by evaluating their likelihood under the probability density function (PDF) corresponding to each narrowband class hypothesis. The class-conditional PDFs are approximated using continuous-state hidden Gauss-Markov models (HGMMs), whose parameters are estimated from labeled training data. Previous HGMM algorithms are extended by dynamically weighting the output covariance matrix by the ratio of the estimated signal and noise energies from each segment. This covariance weighting discounts spurious features from segments with low signal-to-noise ratio (SNR), making the algorithm more robust in the presence of dynamic noise levels and fading signals. The classification algorithm is applied in a simulated three-class cross-validation experiment, for which the algorithm exhibits percent correct classification greater than 97% as low as -7 dB SNR.     

A New Time Domain Technique for Velocity Measurements Using Doppler Ultrasound

A new technique for determining the Doppler frequency shift in a phase-coherent pulsed Doppler system is presented. In the new approach, the Doppler frequency shift is given directly in the time domain in terms of the measured I and Q components of the measured Doppler signal. The algorithm is based on an expression for the instantaneous rate of change of phase which separates rapidly varying from slowly varying terms. It permits noise smoothing in each term separately. Since the technique relies solely on signal processing in the time domain, it is significantly simpler to implement than the classic Fourier transform approach. In addition, the algorithm can be shown to give rigorously accurate values for instantaneous frequency and outperform the Fourier transform approach in poor signal-to-noise environments. Experimental results are presented which confirm the superiority of the new domain technique.     

改进的基于分形盒维数的共信道多信号存在性检测算法

针对时频重叠的共信道多信号存在性检测问题,提出了一种改进的基于分形盒维数的算法。算法用瞬时幅度的盒维数作为检测统计量,通过理论推导得出噪声瞬时幅度的盒维数为一定值1.415,共信道多信号瞬时幅度的盒维数近似等于1,并以此得出了检测的理论门限。如果接收信号瞬时幅度的盒维数小于设定的检测门限则说明有信号,否则没有信号。共信道多信号为MASK、MPSK、MQAM和MFSK任意类型混合时,仿真结果表明在加性高斯噪声背景下算法准确有效,信噪比大于-2dB时检测概率达到100%且虚警概率极低。另外,算法对信号调制类型、调制参数、信号源个数具有很好的鲁棒性,计算简单复杂度低、可实时处理。相比于已有研究,本算法门限值的设定更加精确,检测性能有大幅提升。      

Signal enhancement by time-frequency peak filtering

Time-frequency peak filtering (TFPF) allows the reconstruction of signals from observations corrupted by additive noise by encoding the noisy signal as the instantaneous frequency (IF) of a frequency modulated (FM) analytic signal. IF estimation is then performed on the analytic signal using the peak of a time-frequency distribution (TFD) to recover the filtered signal. This method is biased when the peak of the Wigner-Ville distribution (WVD) is used to estimate the encoded signal's instantaneous frequency. We characterize a class of signals for which the method implemented using the pseudo WVD is approximately unbiased. This class contains deterministic bandlimited nonstationary multicomponent signals in additive white Gaussian noise (WGN). We then derive the pseudo WVD window length that gives a reduced bias when TFPF is used for signals from this class. Testing of the method on both synthetic and real life newborn electroencephalogram (EEG) signals shows clean recovery of the signals in noise level down to a signal-to-noise ratio (SNR) of -9 dB.     

多项式相位信号瞬时频率变化率估计及其应用

通过定义两种新的变换——指数型相位匹配变换和复时间延迟型相位匹配变换,可实现对任意阶次的多项式相位信号(PPS)的瞬时频率变化率(IFR)估计.给出了这两种变换的基本原理和实现方法,同时分析了算法实现过程中应注意的问题.进而,研究了IFR在以下三个方面的应用:(1)PPS的参数估计问题;(2)PPS的瞬时频率估计问题;(3)构造新的时频分布,该分布对于阶次大于2的多项式相位信号具有理想的时频聚集性.最后通过计算机仿真实验验证了本文所提算法的有效性.     

Tracking of unknown nonstationary chirp signals using unsupervisedclustering in the Wigner distribution space

This paper is concerned with the problems of (1) detecting the presence of one or more FM chirp signals embedded in noise, and (2) tracking or estimating the unknown, time-varying instantaneous frequency of each chirp component. No prior knowledge is assumed about the number of chirp signals present, the parameters of each chirp, or how the parameters change with time. A detection/estimation algorithm is proposed that uses the Wigner distribution transform to find the best piecewise cubic approximation to each chirp's phase function. The first step of the WD based algorithm consists of properly thresholding the WD of the received signal to produce contours in the time-frequency plane that approximate the instantaneous frequency of each chirp component. These contours can then be approximated as generalized lines in the (ω, t, t²) space. The number of chirp signals (or equivalently, generalized lines) present is determined using maximum likelihood segmentation. Minimum mean square estimation techniques are used to estimate the unknown phase parameters of each chirp component. The authors demonstrate that for the cases of (i) nonoverlapping linear or nonlinear FM chirp signals embedded in noise or (ii) overlapping linear FM chirp signals embedded in noise, the approach is very robust, highly reliable, and can operate efficiently in low signal-to-noise environments where it is hard for even trained operators to detect the presence of chirps while looking at the WD plots of the overall signal. For multicomponent signals, the proposed technique is able to suppress noise as well as the troublesome cross WD components that arise due to the bilinear nature of the WD     

基于光谱空间信息的高光谱遥感图像混合噪声评估

综合利用高光谱图像的光谱信息和空间信息,提出了一种新的混合噪声评估方法.首先通过滤波算法进行图像中均匀图像块的自动选取;然后利用多元线性回归模型,将均匀图像块内像素点的信号值和噪声值进行分离,并实现了图像中加性、乘性噪声的粗评估;最后根据噪声模型构建似然函数,利用最大似然估计法求解噪声模型参数.通过仿真图像和真实高光谱图像进行实验,验证了该方法的准确性和鲁棒性.