基于WVD的LFM信号检测方法研究

对基于Wigner-Ville分布的LFM信号检测方法进行了总结,推导了加性高斯白噪声背景下的Wigner-Hough变换信噪比公式,并分别采用Wigner-Ville分布和Wigner-Hough变换对单线性调频信号、两线性调频信号进行了检测,然后对检测性能进行了分析。仿真结果表明,与Wigner-Ville分布相比,Wigner-Hough变换可有效抑制高斯白噪声的影响,同时对交叉项也有很好的消除作用。     

基于FRFT的Wigner-Ville分布交叉项抑制方法

针对多分量线性调频信号的魏格纳-维尔分布(Wigner-Ville Distribution,WVD)交叉项干扰问题,提出一种抑制交叉项的方法。该方法利用分数阶傅里叶变换(Fractional Fourier Transform,FRFT)在最佳FRFT域中对给定的线性调频信号具有最好的能量聚集性,将多分量线性调频信号在FRFT域上分解为若干个单分量信号,线性叠加单分量信号的WVD,从而达到抑制交叉项的效果。此外,当多分量线性调频信号为周期信号时周期间存在干扰,进一步提出了在基于FRFT的WVD交叉项抑制方法中增加周期遮蔽处理。仿真结果表明,在保持较高的时频分辨率时,该方法能够有效抑制交叉项,并且有一定的抗噪声能力。     

强鲁棒性和高锐化聚集度的BGabor-NSPWVD时频分析算法

针对短时傅里叶变换（Short-time Fourier transform，STFT）、Gabor变换和魏格纳-维尔分布（Wigner-Ville distribution，WVD）出现的时频分辨率模糊和交叉项干扰，以及目前一些主流改进算法如STFT-WVD和Gabor-WVD存在的频率分量三维幅度失真，且抗噪性能及鲁棒性能不理想等问题，提出基于局部二值化、归一化处理再结合的二值化Gabor-归一化WVD（Binarized Gabor-normalized WVD，BGabor-NWVD）和二值化Gabor-归一化伪平滑WVD（Binarized Gabor-normalized smoothed pseudo WVD，BGabor-NSPWVD）算法.数值仿真实验结果表明，BGabor-NWVD和BGabor-NSPWVD算法较好地抑制了交叉项干扰，具有较高的时频锐化聚集度，且两种算法的抗噪性能和鲁棒性也较为理想.基于本文方法对硬质合金顶锤工作时产生的疑似破裂信号进行时频分析，在抑制噪声和交叉项的同时能够较为准确地寻找传感器的频率判别窗口，为金属破裂监测设备数据采集卡提供有效的阈值参考.     

Local instantaneous frequency estimation of multi-component signals

We present a method for estimating the instantaneous frequency (IF) of multi-component signals. This method involves the calculation of a time–frequency energy density of the signal, then obtaining a local IF estimate from this joint density. Time–frequency energy density is calculated as a least squares optimal combination of multi-window Gabor based evolutionary spectra. The optimal weights are obtained by minimizing an error criterion that is the difference between a reference time–frequency distribution and the combination of evolutionary spectra. IF of the signal components is estimated from the final evolutionary spectrum at small time–frequency regions as the average of frequencies at each time. As such, local IF information of a multi-component signal can be estimated in the time–frequency plane.     

Time–frequency analysis of signals using support adaptive Hermite–Gaussian expansions

Since Hermite–Gaussian (HG) functions provide an orthonormal basis with the most compact time–frequency supports (TFSs), they are ideally suited for time–frequency component analysis of finite energy signals. For a signal component whose TFS tightly fits into a circular region around the origin, HG function expansion provides optimal representation by using the fewest number of basis functions. However, for signal components whose TFS has a non-circular shape away from the origin, straight forward expansions require excessively large number of HGs resulting to noise fitting. Furthermore, for closely spaced signal components with non-circular TFSs, direct application of HG expansion cannot provide reliable estimates to the individual signal components. To alleviate these problems, by using expectation maximization (EM) iterations, we propose a fully automated pre-processing technique which identifies and transforms TFSs of individual signal components to circular regions centered around the origin so that reliable signal estimates for the signal components can be obtained. The HG expansion order for each signal component is determined by using a robust estimation technique. Then, the estimated components are post-processed to transform their TFSs back to their original positions. The proposed technique can be used to analyze signals with overlapping components as long as the overlapped supports of the components have an area smaller than the effective support of a Gaussian atom which has the smallest time-bandwidth product. It is shown that if the area of the overlap region is larger than this threshold, the components cannot be uniquely identified. Obtained results on the synthetic and real signals demonstrate the effectiveness for the proposed time–frequency analysis technique under severe noise cases.     

利用短时分数阶PWVD实现多分量Chirp信号分离

针对加性高斯白噪声背景下多分量Chirp信号的分离问题,采用一种基于短时分数阶傅里叶的伪魏格纳变换来实现对多分量Chirp信号的分离。该方法利用分数阶傅里叶变换四阶中心矩寻找极值点来确定最佳变换域,在最佳变换域对信号进行旋转的短时傅里叶变换,并进行伪魏格纳变换,最后把在时频面得到的冲激信号变换到时域再进行分数阶傅里叶逆变换,实现了多分量Chirp信号的分离。仿真实验证明该方法可有效地实现多分量Chirp信号分离,有助于后续对各分量的参数估计。     

GPS中频信号的建模与仿真

中频信号是软件接收机研制重要输入数据,通常可以通过两种方法获得,一种为通过位捕获器对实际GPS信号进行采集获取,另一种为通过对信号进行数学建模后利用软件生成.相对而言,软件方法更加灵活.通过对GPS L1 C/A码信号的研究,推导和简化了GPS中频信号的建模所需的各种数学模型包括信号生成模型、电离层模型、信号传播时间模型等,并在研究的基础上进行了仿真实现,通过频谱对信号的可信度进行了分析.     

GPS软件接收机信号的快速捕获与跟踪

根据GPS软件接收机对数据块进行信号处理的工作方式,研究基于快速傅里叶变换的循环相关法伪码快速捕获与跟踪技术。基于实测GPS中频数据,采用循环相关法在频域计算输入信号与本地信号的相关值,由相位关系得到精细载频。仿真结果表明,该技术能减少运算量,缩短捕获时间。得到的载频能满足跟踪环对频率分辨率的要求。对跟踪结果进行子帧匹配和奇偶校验后,可以获得导航电文信     

北斗B1频点中频信号仿真系统设计

北斗卫星导航系统是我国自主研发的全球卫星导航系统,属全球四大卫星导航系统之一,开展对北斗信号调制体制及北斗信号的研究具有重大意义。由于北斗卫星导航系统目前处于建设阶段,无法满足全球导航的实际应用需求,且基带信号处理中需要的动态航迹及弱信号环境下卫星中频信号不易获取。为解决此问题,通过对北斗B1频点信号体制及实现方式进行研究,对中频信号进行建模,进而对北斗B1频点中频信号仿真系统进行设计实现。仿真系统可完成基于设定卫星、航迹、载噪比、仿真时间的中频信号仿真并保存,为北斗基带信号处理提供可重复使用的中频数据。     

基于二维快速广义S变换的人脸识别研究

针对人脸识别研究中,离散小波变换分辨率低的缺点及S变换虽分辨率高、方向性多,但计算复杂度高且冗余信息量大的现状,提出了二维快速广义S变换,克服了这些缺点。首先通过编程分析了二维快速广义S域幅度谱及相位谱,并重构原始信号,证明了二维快速广义S变换具有多分辨率和多方向性。其次,将二维快速广义S域的方向性与支持向量机（SVM）结合,将S域低频方向作为SVM的输入特征,实现对ORL标准人脸库中的人脸的识别与分类。实验结果表明：与离散小波变换相比,二维快速广义S变换与SVM相结合应用于人脸识别中,具有更高的识别率。     

Instantaneous frequency based spectral analysis of nuclear magnetic resonance spectroscopy data

Nuclear magnetic resonance spectroscopy signals are modelled as a sum of decaying complex exponentials in noise. The spectral analysis of these signals allowing for their decomposition and the estimation of the parameters of the components is crucial to the study of biochemical samples. This paper presents a novel Gabor filterbank/notch filtering instantaneous frequency (IF) estimator, that enables the extraction of weaker and shorter lived exponentials. This new approach is an iterative procedure where a Gabor filterbank is first employed to obtain a reliable estimate of the IF of the strongest component present. The estimated strongest component is then notch filtered, which un-masks weaker components, and the procedure repeated. The performance of this method was evaluated using an artificial signal and compared to the short time Fourier transform, reassigned STFT, and the original Gabor filterbank approach. The results clearly demonstrate its superiority in uncovering weaker signals and resolving components that are very close to one another in frequency. Furthermore, the new method is shown to be more robust than the ITCMP technique at low signal to noise ratios.     

小波变换在车流量检测雷达信号去噪中的应用

分析了车流量检测雷达中频信号的特点,探讨了小波变换在中频信号去噪中的应用;分别采用小波变换阈值法、平移不变量法和小波变换模极大值法对中频信号进行了去噪处理并对这三种方法的去噪性能进行了分析;实验结果表明,三种方法均能有效的抑制噪声,其中小波阈值法去噪效果较好,计算速度很快;平移不变量法去噪效果略优于小波阈值法,但计算速度较慢,模极大值法虽然取得了最好的去噪效果,但计算速度很慢;综合而言,小波变换阈值法最具实用价值。     

基于分数阶傅里叶变换的LMS自适应滤波

分数阶傅里叶变换是一种线性变换,在多分量情况下不像Wigner-Ville分布那样受到交叉项的影响。但是当信号的信噪比比较小时,检测的效果就比较差,文中提出了一种基于分数阶傅里叶变换的LMS自适应滤波算法。实验结果表明,这种方法在低信噪比的情况下能够有效地检测出信号。另外,如果在自适应过程中采用变步长,可以加快收敛速度,可以显著地减少运算量。     

基于压缩感知的信号时频表示重构

传统的时频分析方法受限于Nyquist采样定理,信息量的增加提高了对采样速率、传输速度和存储空间的要求;同时,双线性魏格纳-维尔分布处理多分量信号时会产生交叉项,常用的核函数法在抑制交叉项时降低了信号的时频聚集性.该文将压缩感知与时频分析方法相结合,在时频分析中突破采样定理的限制,抑制交叉项的同时获得较高的时频聚集性.针对单分量信号、多分量信号、蝙蝠声音信号,利用不同的窗函数如矩形窗或高斯窗,得出仿真结果,验证了基于压缩感知的信号时频表示重构优于传统的基于傅里叶变换进行重构的方法.并利用最小均方误差MSE和时频聚集度CM作为衡量参数,分析了不同样本空间与所重构信号时频表示性能之间的关系.     

小波变换在SPOT5全色与多光谱图像融合中的应用

离散小波变换可以将图像分解成为一系列具有不同分辨率特征、频率特征和方向特性的子带信号,并且将图像的光谱特征和空间特征分离,从而为不同分辨率的遥感图像融合提供了有利条件。采用小波变换融合方法对SPOT5全色与多光谱图像进行融合处理,以提高图像的空间信息质量和光谱质量为目的。通过对融合结果图像进行主观和客观的综合评价,可得出融合前后图像灰度均值变化很小,灰度标准差、熵和清晰度三者变化趋势一致;当小波分解层数为3时,融合图像的光谱质量与空间质量之间达到较好的平衡,同时融合图像的视觉效果良好。     

网络流量行为分析的小波加权混沌局域模型

结合小波变换和混沌局域模型的各自优势,提出一种网络流量的预测模型。首先,将网络流量时间序列进行小波分解得到高频信号序列和低频信号序列,再用加权混沌局域模型对每一成分的信号序列分别进行预测,对所有的预测分量进行小波重构就可以实现对网络流量的预测。用实际网络流量对该模型进行验证,实验结果表明,该模型具有较高的预测效果。     

基于离散小波变换的信号分解算法研究

提出一种基于离散小波变换的信号分解算法.实验结果表明,该算法较好地克服了传统信号分解在高频段的频率分辨率较差,而在低频段的时间分辨率较差的缺点。     

基于粒子群算法的跳频信号参数估计*

针对基于时频分布的参数估计存在信噪比阈值和低信噪比下方差大的问题,提出了一种基于多峰优化粒子群算法的跳频信号参数估计新算法。该算法首先将跳频信号分解为时频原子的线性组合,然后由匹配原子获取跳频信号的参数估计。仿真结果表明,基于改进的物种形成粒子群算法能够搜索到与跳频信号分量相匹配的原子,与平滑伪魏格纳分布相比,提出的参数估计算法在低信噪比下具有较小的估计方差,更加适宜于电子战的实际应用。     

陶瓷基复合材料粘接构件超声特征研究方法

新型航天器机体外层陶瓷基复合材料隔热构件在粘贴过程中会产生缺陷,不利于航天器的飞行安全.针对一种陶瓷基多孔复合材料粘接结构构件,利用自主研制的超声C扫描自动检测系统,研究构件粘接层缺陷的超声检测特征.考虑材料多孔性和各向异性会导致超声数据多噪音,采用自适应小波阈值算法对超声信号进行去噪,从而提高透射声波的信噪比.在此基础上,提出采用Hilbert-Huang变换(HHT)和伪Wigner-Ville分布(PWVD)相结合的方法对透射声波进行时频分析,得到时间、频率和幅值的三维分布,从而获得超声透射波最大幅值和对应的时间和频率.通过对比缺陷处与非缺陷处超声透射信号的时频域分布,揭示了该类新型陶瓷基复合材料粘接缺陷对超声信号的影响特点.     

Phase analysis and statistical modeling with limited batches for multimode and multiphase process monitoring

For multimode batch processes, the conventional modeling methods in general require that sufficient batches should be available for every mode, which, however, cannot be guaranteed in practice. It may be impractical to conduct enough trial runs and wait until sufficient batches are available before development of monitoring models for each mode. Starting from limited batches, how to derive reliable process information and develop monitoring models has been an important question for successful online multimode batch process monitoring. To address this problem, this article proposes a phase analysis and statistical modeling strategy with limited batches. One mode which has obtained sufficient batches is chosen as the reference mode while the other modes which can only get limited batches work as alternative modes. Starting from limited batches, the proposed algorithm addresses two issues, concurrent phase partition and analysis of between-mode relative changes. First, for each alternative mode, generalized time-slices are constructed by combining several consecutive time-slices within a short time region to explore local process correlations. The time-varying characteristics are then concurrently analyzed across modes so that multiple sequential phases are identified simultaneously for all modes. Then phase-representative data units are arranged by variable-unfolding the conventional time-slices for the reference mode and the generalized time-slices for each alternative mode respectively. Between-mode statistical analysis is performed within each phase where the relative changes from the reference mode to each alternative mode are analyzed. From the between-mode perspective, different types of relative variations in each alternative mode are separated and modeled for online monitoring. Starting from limited batches, online batch process monitoring can be conducted, providing reliable fault detection performance. The proposed algorithm is illustrated with a typical multiphase batch process with multiple modes.