基于小波脊线-Hough变换的LFM信号检测

线性调频(LFM)信号是现代雷达广泛使用的一种大时宽-带宽积的低截获概率信号,根据线性调频信号的小波变换特性,小波脊线与瞬时频率的对应关系,提出了一种检测线性调频信号的联合小波脊线-Hough变换方法,该方法首先计算信号的小波变换,得到二维时-频能量分布图,采用脊算法提取信号的小波脊线,然后在小波脊线时-频平面上再进行Hough变换,从而检测噪声中的线性调频信号并估计信号参数.仿真结果证明,此方法可有效地对线性调频类信号进行检测,并且有较好的抗噪声性能.     

基于时频分布盲辨识算法的电磁干扰信号分析

提出一种可用于分离不同时频分布的非平稳信号的盲信号辨识算法。采用Wigner－Ville分布（WVD）进行盲源分离时,合成信号有交叉项存在,其分离性能不理想。而Cohen 类时频分布可以抑制交叉项,并且保持时频聚集性。因此,在TFBSS 中,Cohen 类时频分布可以取得更好的分离性能。分析了Cohen 类时频分布对交叉项的抑制性能,以及对盲源分离性能的影响,结果表明：采用盲辨识算法进行电磁干扰信号分离,其效果明显优于采用WVD进行分离的效果。     

A method for estimating the parameters of the K distribution

A method that combines the maximum likelihood and the method of moments for estimating the parameters of the K distribution is proposed. The method results in the lowest variance of parameter estimates when compared with existing non-ML techniques     

基于遗传算法和时频分布的跳频信号盲分离

根据跳频信号的非平稳特性,采用一种基于时频分布的跳频信号盲分离算法,通过合理选择符合＂单个自项＂要求的时频点,将盲源分离问题转化成对一组时频分布矩阵联合对角化的数学优化问题,由于遗传算法具有良好的全局寻优能力,因此利用遗传算法对能够表征矩阵联合对交化效果的代价函数进行优化求解,寻找能将矩阵组联合对角化的权矩阵。仿真结果...     

基于重排时间尺度分布的跳频信号参数盲估计

提出了一种低截获概率跳频信号的截获方法,该方法基于信号小波尺度谱的重排时间尺度分布,在未知任何先验条件情况下,能够检测并提取非合作跳频信号的跳频频率、跳频周期和跳变时刻特征参数信息。文中比较了跳频信号的小波尺度谱与重排小波尺度谱分析效果,提出了基于重排小波尺度谱的跳频信号参数盲估计算法,通过计算机仿真得到了较为准确的估计结果,并对算法的性能进行了分析,验证了该方法的有效性。     

基于科恩分布的地球物理信号的时频分析

在简要讨论几种科恩类广义双线性时频分布的基础上,探讨双线性时频分布的算法实现与仿真流程,进而模拟出地磁脉动信号的几种时频分布,同时还对Pc脉动的几种双线性时频分布进行了比较,给出了相应的交叉项抑制和参数选取的方法。结果表明,在核函数参数选取适当的情况下,广义双线性时频表示是分析地球物理信号的有力工具。     

线性调频信号检测方法的研究

在线性调频信号体制雷达的数字脉冲压缩系统中对数字脉压后的脉冲串进行DFT相参处理时，若采样时刻偏离脉压回波的峰值时刻，不同的采样频率使得DFT相参积累后的信噪比会有不同程度的损失。本文详细分析了采样频率分别为20MHz和10MHz时对信号相参积累的影响，并对幅值检测和M／N检测两种检测方法的检测性能进行了比较，仿真结果表明，M／N检测优于幅值检测。     

基于时频分布图像和主分量分析的脉内调制识别算法研究

本文提出了一种利用模式识别中图像处理的方法来进行雷达脉内调制特征识别的新算法.通过对雷达信号时频分布图像进行二维小波分解,并对其进行主分量分析,获得每类雷达信号中的特征参数,构建相应的分类器,并利用此分类器对信号进行识别.仿真结果表明,利用信号的时频分布图像来进行信号识别是完全可行的,提取的特征参数具有很好的鲁棒性,具有较强的抗噪声能力,同时可以取得较高识别率.     

基于任意阵列形式的LFM信号参数估计

基于任意阵列形式的LFM信号参数估计算法,利用分段解线调建立频域波束空间数据模型,通过Beamspace—ESPRIT算法和线性调频波束形成器算法(CBF)实现了调频斜率和初始频率的高精度估计。针对多信号交叉项的影响,提出了逐次滤波和迭代滤波的解决办法。算法对线性调频参数无模糊估计的条件进行了研究,并给出了欠采样条件下初始频率估计解模糊的方法。仿真实验证明了算法的有效性。     

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。     

中频采样的线性调频脉冲信号恢复高频信号的研究

提出了一种将中频采样信号恢复为高频信号的算法,证明了线性调频脉冲信号的频谱特性与信号中频无关,给出了方法可行的限制条件。     

The use of BDS statistics for estimating the parameters of chaotic mappings and regular signals in the presence of noise

A new method has been proposed in this paper for estimating the parameters of chaotic and regular signals by their observation against the background of white noise under conditions of the a priori uncertainty about the distribution of its values. The method is based on using the nonparametric BDS statistic revealing the sensitivity toward topological properties of the attractors of chaotic, regular and random processes that are characterized by the correlation dimension. The results of numerical simulation of the method proposed for the estimation of parameters of one-dimensional and two-dimensional chaotic mappings and also the harmonic oscillation frequency for the noise with uniform and Gaussian distribution at different levels of its intensity have been presented. This study also includes the analysis of accuracy of estimating the harmonic oscillation frequency by the proposed method and its comparison with potentially attainable values.     

DOA estimation of LFM signal based on Krylov subspace method

Telecommunication Systems - Direction of arrival estimation of LFM signal is an essential task in radar, sonar, acoustics and biomedical. In this paper, a short time Fourier transform multi-step...     

基于欠采样的宽带线性调频信号参数估计

基于中国余数定理解频率模糊和解线调方法,提出一种无模糊欠采样的宽带线性调频信号的调频斜率和初始频率的估计算法.首先,欠采样宽带线性调频信号与其两路不同延迟共轭相乘,根据两路输出信号的单频特性,利用FFT、谱峰检测(PSD)和中国余数定理进行宽带线性调频信号的调制斜率无模糊估计;其次,根据估计的调制斜率数字合成无载波的线性调频信号对另外两路不同延迟的欠采样宽带线性调频信号解线调处理,利用类似方法进行宽带线性调频信号的初始频率无模糊估计.利用频谱细化技术,参数估计精度可进一步提高.计算机仿真证实了算法的有效性.     

Underdetermined blind separation of non-disjoint signals in time-frequency domain based on matrix diagonalization

To estimate precisely the mixing matrix and extract the source signals in underdetermined case is a challenging problem, especially when the source signals are non-disjointed in time-frequency (TF) domain. The conventional algorithms such as subspace-based achieve blind source separation exploiting the sparsity of the original signals and the mixtures must satisfy the assumption that the number of sources that contribute their energy at any TF point is strictly less than that of sensors. This paper proposes a new method considering the uncorrelated property of the sources in the practical field which relaxes the sparsity condition of sources in TF domain. The method shows that the number of the sources that exist in any TF neighborhood simultaneously equals to that of sensors. We can identify the active sources and estimate their corresponding TF values in any TF neighborhood by matrix diagonalization. Moreover, this paper proposes a method for estimating the mixing matrix by classifying the eigenvectors corresponded to the single source TF neighborhoods. The simulation results show the proposed algorithm separates the sources with higher signal-to-interference ratio compared to other conventional algorithms.     

New method of bad points elimination based on Hough transform

In experimental tests, besides data in range of allowable error, the experimenters usually get some unexpected wrong data called bad points. In usual experimental data processing, the method of bad points exclusion based on automatic programming is seldom taken into consideration by researchers. This paper presents a new method to reject bad points based on Hough transform, which is modified to save computational and memory consumptions. It is fit for linear data processing and can be extended to process data that is possible to be transformed into and from linear form; curved lines, which can be effectively detected by Hough transform. In this paper, the premise is the distribution of data, such as linear distribution and exponential distribution, is predetermined. Steps of the algorithm start from searching for an approximate curve line that minimizes the sum of parameters of data points. The data points, whose parameters are above a self-adapting threshold, will be deleted. Simulation experiments have manifested that the method proposed in this paper performs efficiently and robustly.     

Comparison of time-frequency distribution techniques for analysisof simulated Doppler ultrasound signals of the femoral artery

The time-frequency distribution of the Doppler ultrasound blood flow signal is normally computed by using the short-time Fourier transform or autoregressive modeling. These two techniques require stationarity of the signal during a finite interval. This requirement imposes some limitations on the distribution estimate. In the present study, three new techniques for nonstationary signal analysis (the Choi-Williams distribution, a reduced interference distribution, and the Bessel distribution) were tested to determine their advantages and limitations for analysis of the Doppler blood flow signal of the femoral artery. For the purpose of comparison, a model simulating the quadrature Doppler signal was developed, and the parameters of each technique were optimized based on the theoretical distribution. Distributions computed using these new techniques were assessed and compared with those computed using the short-time Fourier transform and autoregressive modeling. Three indexes, the correlation coefficient, the integrated squared error, and the normalized root-mean-squared error of the mean frequency waveform, were used to evaluate the performance of each technique. The results showed that the Bessel distribution performed the best, but the Choi-Williams distribution and autoregressive modeling are also techniques which can generate good time-frequency distributions of Doppler signals     

基于欧氏距离图的随机Hough变换椭圆检测方法

为了实现椭圆目标的有效检测,克服椭圆检测过程中对椭圆完整性和边缘梯度精度要求过高的缺点,提出了一种改进的随机Hough变换的椭圆检测方法。首先充分利用椭圆的轴对称特性和极点-极弦性质求取候选椭圆,有效解决了无效采样和累积问题,然后采用欧氏距离图计算椭圆边缘点的欧氏距离之和来确定真实椭圆。实验结果表明,该算法相对于RHT-3算法和CMHT算法具有检测精度高、检测速度快和抗椭圆缺失能力强的优点。