Instantaneous Frequency Estimation of Nonlinear Frequency-Modulated Signals Under Strong Noise Environment

Instantaneous frequency (IF) is the most important parameter of a signal, which is an important representation of non-stationary signals, such as frequency-modulated signals. Usually, signals are received with noises. Under noise environment, the conventional IF estimation methods for nonlinear frequency-modulated (NLFM) signal cannot work. In this paper, we focus on how to extract IF of NLFM signal under strong noise environment. First, a modified S-method (SM) is proposed to represent the time–frequency (TF) characteristic. The modified SM uses an adaptive smooth window. The symmetric window is used for multi-component signals and asymmetric window for mono-component signals. The modified SM enhances the TF energy concentration and suppresses the cross-terms effectively. Then, the Viterbi algorithm is used to extract the IF from the TF plane. Viterbi algorithm is a hidden Markov chain approach, which is proposed here as the IF estimator. The proposed method is utilized for various types of NLFM signals. Simulation results demonstrate the efficiency and validity of the proposed method under strong noise environment.     

基于分数阶傅里叶变换的多分量Chirp信号的检测与参数估计

分数阶傅里叶变换是傅里叶变换的广义形式。利用Chirp信号在分数阶傅里叶变换域的特点,提出了对含有多个非平稳Chirp成分的信号在噪声中的检测与参数估计方法。理论分析和仿真结果表明,与现有的基于时间域、频率域和时频域的方法相比,该方法物理意义清楚,计算简便,无交叉项干扰,抗噪声性能强。     

基于小波-Radon变换的线性调频信号检测与参数估计

线性调频信号（LFM）是一类应用广泛的非平稳信号.本文选取高斯线调频小波作为基函数,研究了基于小波-Radon变换的线性调频信号检测与参数估计的基本方法,然后提出了基于小波-Radon变换的多分量LFM信号检测与参数估计的算法.计算机仿真实验结果验证了该算法的有效性。     

一种基于Radon变换的时频分析方法

针对CW脉冲和线性调频(LFM)信号,利用Radon变换沿直线积分的特性,将其与时频分布(TFD)结合在一起,抑制多频率分量信号各个分量之间的交叉项干扰,提高时频分布的时频二维分辨力。通过仿真数据验证算法具有良好的时频分辨能力以及抑制交叉项干扰能力。     

Adaptive optimal kernel smooth-windowed Wigner-Ville bispectrum for digital communication signals

Higher-order time-frequency distribution (HO-TFD) outperforms the bilinear TFD in noisy conditions but suffers more severely from cross-terms when used to analyze multi-component signals. Various kernel functions have been introduced to suppress cross-terms in bilinear TFD but in general TFD with a fixed kernel do not give accurate TFR for all type of signals. In this paper, adaptive optimal TFR is obtained by extending the separable kernel design in bilinear TFD to the third-order TFD and is able to achieve accurate time-frequency representation at SNR as low as −2 dB. This globally adaptive optimal kernel smooth-windowed Wigner-Ville bispectrum (AOK-SWWVB) is designed where its separable kernel is determined automatically from the input signal, without prior knowledge of the signal parameters. It is shown that this system performance is comparable to the system when priori knowledge of the signal is known.     

高斯白噪声背景下的LFM信号的分数阶Fourier域信噪比分析

目标大机动运动使雷达回波表现为频率和调频率参数均未知的LFM信号。未知参数LFM信号的检测和估计采用分数阶Fourier变换来实现受到越来越多的关注,为此本文着重分析其分数阶Fourier变换的信噪比。首先推导出时限线性调频信号的分数阶Fourier变换模平方,给出了在分数阶Fourier域的峰值点与未知参数的关系,然后研究了附加白噪声LFM信号在分数阶Fourier域的统计特性,确定了其信噪比,并与理想情况(即参数频率和调频率参数已知)下线性相位匹配滤波器的输出信噪比进行了比较。     

Modernized method of estimating the parameters of LFM signals based on the time-frequency distribution correction and the use of the Hough transformation

The method has been modernized for obtaining the parameter estimation of the fine structure of LFM signals (linear-frequency-modulated signals) at small values of the signal-to-noise ratio. The development of this method was based on the analysis of the signal time-frequency distribution (TFD) and the Hough transform. The specific feature of this method is correction of the time-frequency parameters in the TFD image considered and the use of the principle of detecting the straight line by the Hough transform that allows us to obtain estimates of parameters of LFM signals from the radio signal received within a shorter time interval at small signal-to-noise ratios. The results of simulation modeling demonstrate the capabilities of the method proposed.     

基于Radon-Wigner变换的非平稳信号分析

采用Radon-Wigner变换方法,实现了噪声背景下单分量线性调频信号和多分量线性调频信号的检测。仿真结果表明,对于线性调频信号检测,Radon-Wigner变换方法性能明显优于Wigner-Ville分布法。     

非平稳信号分析的广义解析模态分解方法

现有的非平稳信号分析方法都有各自不同的缺陷,短时傅里叶变换的时频分辨率受不确定性原理的限制,希尔伯特黄变换存在端点效应和模态混叠,易导致模糊的时频分布;解析模态分解只适合分析频率恒定的多分量信号;针对包含多个时变模态、特别是频谱重叠的非平稳信号,本文提出了一种新的信号分析方法———广义解析模态分解（Generalized Analytical Mode Decomposition,GAMD）．GAMD通过广义傅里叶变换将时变频率转换为频谱可分的,采用解析模态分解对其分解,再对得到的单分量信号进行逆广义傅里叶变换即可得到原始信号的分量．因此,GAMD非常适合分析时变的非平稳信号．通过仿真信号将GAMD与短时傅里叶变换和希尔伯特黄变换等方法进行了对比,结果表明GAMD方法的分解效果更精确,时频分辨率更高．     

Parameter estimation for micro-Doppler signals based on cubic phase function

An estimator for evaluating the parameters from the radar returned multicomponent micro-Doppler (m-D) signals is presented in this paper. While time frequency distribution (TFD) is commonly used to analyze the time-varying m-D frequency features in TF domain, the proposed algorithm is based on cubic phase function (CPF) that can transform the signal to time frequency rate domain. In order to estimate the parameters of multicomponent m-D signal, the extended Hough transform (HT) of CPF is employed to estimate linear frequency modulation (LFM) or sinusoidal frequency modulation (SFM) components. For the m-D signal composed of both LFM and SFM components, the estimates involve two steps of HT-CPF. Firstly, LFM components are estimated by HT-CPF and removed from the time frequency rate plane, and then, HT of the modified time frequency rate distribution is applied to estimate SFM ones. Compared with HT-TFD, this algorithm needs less dimension of HT space and is thus computationally efficient. In addition, simulations show that the algorithm has almost the same performance signal-to-noise threshold as HT of Wigner–Ville distribution method.     

基于积分二次相位函数和分数阶Fourier变换的多分量LFM信号参数估计

针对高斯白噪声中多分量线性调频信号参数估计问题,提出了一种基于积分二次相位函数（IQPF）和分数阶Fourier变换的新方法。分析了IQPF估计线性调频信号调频率的原理,指出IQPF有压制弱信号的缺点。为解决强度相差较大的多分量线性调频信号中弱分量信号的参数估计问题,提出利用分数阶Fourier变换域的信号分离技术,逐次估计强信号分量的参数并将其消去,来提高多分量信号参数估计的可靠性。最后通过计算机仿真,验证了该方法的有效性。这种方法与Radon-Winger变换法、Radon-Ambiguity变换法和单纯的分数阶Fourier变换法相比,极大的简化了计算。因此,该方法非常适合于多分量LFM信号的快速参数估计。      

基于RPRG+ICCD与RANSAC的多分量多项式相位信号参数估计

多分量多项式相位信号的检测和参数估计是多项式相位信号处理的一大难点。时频分析方法能够通过时频分布表征多项式相位信号的瞬时频率,通过对提取的瞬时频率进行多项式拟合得到其系数,经过简单的变换即可获得多项式相位信号的参数。文中利用RPRG+ICCD分离多分量多项式相位信号得到各个分量的瞬时频率,再使用RANSAC进行多项式拟合,最终实现了多项式相位信号的参数估计。实验结果表明,文中提出的方法能够实现在时频域部分重叠的多分量多项式相位信号的分离和参数估计。     

基于时频重排-Hough变换的多分量Chirp信号的检测与参数估计

针对BTFD-Hough算法因交叉项形成伪尖峰引起的误检测;而平滑交叉项又造成参数估计精度降低和运行 时间增加的问题,本文提出了对BTFD进行时频重排再结合Hougb变换的多分量Chirp信号检测与参数估计的改进方法, 仿真实验结果表明,该方法不仅有效地检测多分量Chirp信号并估计其参数,还降低了H0ugIl变换时间,同时也改善了抗 噪声性能。     

基于分数阶Fourier变换的多分量LFM信号检测

分数阶Fourier变换作为最新提出的一种分析工具,其变换域同时具有信号的时域信息和频域信息,其实质是Fourier变换的一种广义形式,较适合处理非平稳信号。文中提出一种基于分数阶Fourier变换的多分量LFM信号参数估计与分离方法。通过在分数阶Fourier域搜索峰值点来对多分量LFM信号进行检测和参数估计,同时结合逐次消去思想来分离多个未知参数的LFM信号,抑制了强信号分量对弱信号分量的遮蔽干扰。     

An Integral Transform and Its Applications in Parameter Estimation of LFM Signals

This paper proposes a new transform called simplified linear canonical transform (SLCT) that provides a new method for parameter estimation of linear frequency-modulated (LFM) chirp signals embedded in additive white Gaussian noise. The proposed transform is a linear transform and has a more succinct form as compared with the fractional Fourier transform (FRFT). The discrete SLCT with fast Fourier transform (FFT) algorithm provides a computationally fast choice for LFM signal detection or parameter estimation. Using SLCT and a clean technique, all the components of Multi-LFM signals can be estimated seriatim. Simulations illustrate that the proposed algorithm is more effective than existing ones.     

非高斯非平稳背景噪声中的信号检测研究

非高斯非平稳噪声的干扰问题在通信过程中是经常出现的。在非高斯非平稳背景噪声下,以前经常使用经典信号检测理论对信号进行检测,很难取得较为理想的效果。基于小波变换以及小波去噪原理,提出一种新的阈值处理方法,该方法能有效地去除噪声,使有用信号能从非高斯非平稳噪声中检测出来。实验结果表明,新方法不但去噪效果明显,而且获得了较高的分辨率和信噪比,检测性能较为理想,是对信号检测理论的一种有效推广。     

基于熵的Gabor变换窗函数宽度自适应选择算法

该文针对Gabor变换中窗函数宽度选择的问题,提出了以提高Gabor表示的聚集性和时频分辨率为目的的窗函数宽度自适应选择算法。提出对香农熵的取值范围进行改进,使其更适合度量时频分布的聚集性,进而根据熵度量实现了与信号非平稳性相适应的最优窗函数宽度选择。仿真结果表明该算法对单分量及多分量信号都能有效地选择最优窗函数宽度,能够获得聚集性好、时频分辨率高的Gabor表示,并具有很好的抗噪性能。     

基于稀疏时频分布的跳频信号参数估计

基于常规时频分析方法的跳频信号参数估计中,采用核函数抑制时频分布交叉项会导致时频聚集性的下降,不利于信号参数提取。针对此问题,该文提出一种基于稀疏时频分布(STFD)的跳频信号处理方法。该方法首先根据Cohen类分布的原理和跳频信号模糊函数的特点,以模糊域矩形窗为核函数,构建了一种Cohen类的矩形核分布(RKD)。RKD可有效抑制交叉项,但其时频分辨率较低。为提高RKD的时频性能,在压缩感知框架下,利用跳频信号时频分布的稀疏特性,对RKD附加稀疏性约束,建立稀疏时频分布(STFD)的优化求解模型。STFD不仅能有效抑制交叉项,而且具有良好的时频聚集性。仿真分析表明,与传统时频分析方法相比,该文提出的基于STFD的跳频信号参数估计方法性能更优。     

Enhanced Speech Based Jointly Statistical Probability Distribution Function for Voice Activity Detection

Most of Voice activity detection (VAD) methods are based on statistical model. In these meth-ods, the noise signal is always assumed to satisfy and characterized by Gaussian distribution, while the assump-tion of noise does not always hold in practice and which causes that these kinds of method fail to distinguish speech from noise at low Signal-noise-ratio (SNR) level in non-stationary noise condition. For going further to improve the robustness of VAD, a enhanced speech based method is proposed. In the proposed method, the Laplacian distri-bution is used to model the remained noise since we find that the remained noise in enhanced speech satisfy Lapla-cian distribution; in addition, Gaussian mixture model is used to characterize the Discrete Fourier transform (DFT) coefficients of reconstructed speech in enhanced speech. Experimental results show that the proposed method per-forms better than the baseline method, especially in low SNR and non-stationary noise conditions.     

基于REPT变换和空间EP谱的宽带LFM 信号参数估计

演化谱估计作为研究非平稳信号的重要手段,具有许多优良的性质。将演化谱应用于参数估计和宽带阵列信号处理中,提出了Radon-EP（Evolutionary Periodogram）谱变换（REPT）估计线性调频信号参数和空间分段短时EP谱估计宽带线性调频信号到达角。仿真实验显示了算法的有效性。