基于MF-FRFT伪码捕获算法研究

针对高动态环境下接收机的接收信号含有较大多普勒频率及其变化率,传统捕获方法无法对多普勒频率变化率进行有效补偿等问题,提出了一种基于分数阶傅里叶变换的捕获方法。所提算法尝试采用匹配滤波器与分数阶傅里叶变换相结合的方法,利用在高动态环境下载波多普勒呈现近似线性调频信号的特点,寻找变换后峰值所在位置即可对多普勒频率和频率变化率进行有效估计,无需进行频域搜索,大大节省了捕获时间。理论分析和仿真验证表明,本文提出的基于分段分数阶傅里叶变换捕获方法,解决了传统方法在高动态环境下难以对信号多普勒变化率进行有效补偿的问题,尤其在低信噪比情况下,更好地提高了接收机捕获概率、缩短了捕获时间。     

基于短时分数阶傅里叶变换的语音增强算法

提出了一种基于短时分数阶傅里叶变换(STFRFT)的语音增强新方法.该方法首先将带噪语音信号进行短时分数阶傅里叶变换,然后在分数阶傅里叶域(FRFD)对信号进行滤波,最后对滤波后的信号进行短时分数阶傅里叶逆变换,得到增强后的语音信号.实验表明在选定最佳的分数阶阶数时,可使噪声得到最大限度的滤除,大大提高了语音增强效果.     

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。     

FRFT域的数字图像chirp噪声抑制方法

针对含有chirp噪声的图像,应用传统的滤波方法难以实现有效的信噪分离,提出了一种基于分数阶傅里叶变换域的数字图像chirp噪声的抑制方法。该方法是将chirp信号分数阶傅里叶域的滤波算法引入到数字图像的处理中,是一种新的改善图像质量的手段。仿真结果表明,对于含有chirp噪声干扰这一特定退化模型的图像,采取最优估计下的分数阶傅里叶变换相比普通傅里叶变换和线性平滑滤波,图像恢复的效果更佳,它能有效地去除图像中的chirp噪声。     

基于分数阶傅里叶变换的宽带LFM相干信号的DOA估计

提出一种基于分数阶傅里叶变换(FRFT)的宽带线性调频信号(LFM)相干信号的波达方向(DOA)估计方法。针对分数阶傅里叶变换的谱峰聚集特点,该算法构造了分数阶傅里叶域(FRF域)的宽带LFM信号的阵列数据模型,在FRF域内利用Toeplitz矩阵重构特性,实现对相干信号的解相干,最后通过谱峰搜索完成入射信号的DOA估计。算法在不减小阵列有效孔径的情况下,增加了可估计相干信号源数目,并在低信噪比条件下能够得到较好的性能。实验仿真证明了该算法的有效性。     

一种新的宽带LFM信号到达角估计

利用分数阶傅里叶变换(FrFT)良好的信号选择性和抗干扰能力,提出了基于FrFT-自聚焦的宽带LFM(线性调频)信号到达角估计(DOA)方法,实现宽带LFM信号DOA高分辨率估计,仿真实验验证了新方法的有效性。     

周期线性调频干扰信号分数阶频谱分析与参数估计方法

针对跨周期的线性调频干扰信号的检测和参数估计问题,提出一种分数阶频谱分析与参数估计方法。以采样数据横跨2个周期为例,分析采样的周期线性调频干扰信号在分数阶傅里叶变换域的频谱分布特征,2个周期内的频谱重叠宽度与分数阶旋转角α和各周期内数据长度的关系,以及频谱尖峰发生偏移的条件。推导在离散分数阶傅里叶变换中各参数间的相互关系,得出采用快速解线性调频技术预估调频率的原因。实验结果表明,在2种仿真情况下,该方法都具有较高的参数估计精度。     

短时分数阶傅里叶变换的基本性质

作为一种不会对信号时频结构在解线调时产生压缩扭曲的线性时频分析工具,短时分数阶傅里叶变换(STFrFT)相比于分数阶傅里叶变换更适于处理多项式相位信号.证明了短时分数阶傅里叶变换的一些基本性质,例如:重构条件和帕塞瓦尔定理等.以chirp信号为例给出了STFrFT的窗函数和窗口参数的选择依据.本文结论为短时分数阶傅里叶变换的应用提供参考.     

基于分数阶傅里叶变换的宽带LFM相干信号二维DOA估计

在分数阶傅里叶(FRF)域从离散角度推导了二维DOA估计数学模型,并在此模型基础上提出基于分数阶Fourier变换的二维相干信号DOA估计新算法.该方法利用分数阶傅里叶变换良好的能量聚集性,在分数阶傅里叶(FRF)域构造前后向空间平滑DOA矩阵.通过对DOA矩阵进行特征值分解,估计信号子空间和噪声子空间,由信号子空间的特征值和特征向量得到宽带LFM相干信号的二维到达角,避免了二维谱峰搜索和交叉项,也无需参数配对.理论推导与实验仿真验证了算法的有效性.     

基于分数阶傅里叶变换的多项式相位信号参数估计

研究了一种基于分数阶傅里叶变换(FRFT)的多项式相位信号快速估计方法,对于线性调频信号(LFM),即用信号延时相关解调的方法得到调频斜率的粗略估计,从而得到分数阶旋转角度的范围,简化为小范围的一维搜索问题。多项式相位信号的检测通过延时相关解调可转化为LFM信号的检测,再运用FRFT便可进行参数估计。理论分析与仿真结果表明该方法简单,估计性能好。     

MIMO SAR OFDM chirp waveform design and GMTI with RPCA based method

Multiple-input multiple-output synthetic aperture radar (MIMO SAR) has drawn wide attention for its increased degrees of freedom (DOFs) compared to the traditional multi-channel SAR (MC-SAR) system. The MIMO SAR system, which makes each antenna illuminate one subswath, can widen the range swath without decreasing PRF. One of the foremost tasks involved in the MIMO SAR system is the waveform design at the transmitter. Moreover, at the receiver, how to accurately separate the auto-correlation signal from the cross-correlation interferences is the other important issue. In this paper, an orthogonal frequency division multiplexing (OFDM) chirp signal designing method is firstly proposed, which is based on low cross-correlation interferences and good peak-to-sidelobe ratio (PSLR) rules. Using the designed OFDM chirp signals, the focused signal of MIMO SAR system matched the robust principal component analysis (RPCA) basic model. The moving target (sparse signal), clutter (low-rank signal) and noise (noise signal) can be separated directly. Hence, RPCA based method can be employed for ground moving target indication (GMTI) with no need for extracting the matched signal or suppressing the unmatched interferences. Extensive simulations demonstrate the effectiveness of the waveform designing method and GMTI with RPCA based method for MIMO SAR system.     

基于SVD-EMD的有效微弱信号提取算法

针对奇异值差分谱的信号提取方法不能有效提取出强噪声环境中的微弱信号这一问题,提出了一种基于奇异值分解(SVD,singular value decomposition)和经验模态分解(EMD,empirical mode decomposition)相结合的微弱信号提取方法。采用奇异值差分谱选择奇异值进行信号重构,提取出带噪部分信号,对其进行EMD分解和SVD降噪处理,叠加得到降噪后的信号。仿真结果表明,该方法能够准确提取微弱信号信息,并能有效地去除信号噪声。     

采用SVD-NMF的管道泄漏信号去噪算法

在管道泄漏检测中,压力信号中的噪声干扰会降低传统互相关法的定位精度。传统的去噪算法对环境的适应性差,去噪效果不理想。为此,提出了一种奇异值分解SVD( Singular Value Decomposition)与非负矩阵分解NMF( Nonnegative Matrix Factorization)相结合的管道泄漏信号去噪算法。该方法首先通过奇异值分解确定非负矩阵分解的阶数并对其初始化;然后,采用改进的非负矩阵分解算法对原信号进行迭代分解,获得去噪信号;最后,对去噪信号进行处理后通过互相关计算时延,并结合泄漏信号的传播速度实现泄漏定位。大量实验结果表明,SVD￣NMF算法能够显著降低迭代次数,提高去噪速度;同时在泄漏检测中,能够达到去除噪声干扰,提高定位精度的目的。     

基于CEEMDAN的MEMS加速度计输出信号降噪方法

针对微机电系统(MEMS)加速度计输出信号存在误差,导致高压输电杆塔倾斜监测系统的输出倾角数据精确度不高的问题,提出了一种基于自适应噪声完备集合经验模态分解(CEEMDAN)联合奇异值分解(SVD)对杆塔的加速度计输出信号降噪方法。利用CEEMDAN对原始加速度计输出信号进行分解,得到一系列模态分量,分别计算其排列熵(PE),筛选出特征分量和含噪特征分量,然后再将需进一步降噪的特征分量通过SVD进行二次滤波,最后将降噪后的特征分量与未处理的特征分量进行叠加即得到降噪后的加速度计输出信号。仿真实验结果表明,所提出的方法可以有效地抑制噪声干扰,通过与扩展卡尔曼滤波和CEEMDAN-PE对比说明该方法滤波效果更好,有效提高了加速度信号分析精度和杆塔倾斜角测量精度。     

Time domain signal enhancement based on an optimized singular vector denoising algorithm

This paper presents a new time domain noise reduction approach based on Singular Value Decomposition (SVD) technique. In the proposed approach, the noisy signal is initially represented in a Hankel Matrix. Then SVD is applied on the Hankel Matrix to divide the data into signal subspace and noise subspace. Since singular vectors are the span bases of the matrix, reducing the effect of noise from the singular vectors and using them in reproducing the matrix leads to considerable enhancement of information embedded in the matrix. The noise-reduced singular vectors from the signal subspace are utilized to reconstruct the data matrix. This matrix is finally used to obtain the time-series signal. The results of applying the proposed method to different synthetic noisy signals indicate a better efficiency in noise reduction compared to the other time series methods.     

Modeling and characteristic analysis of underwater acoustic signal of the accelerating propeller

The signal feature of propeller cavitation noise during acceleration or deceleration procedure can be used to passively detect and classify moving vessels and underwater vehicles in the port regions.By analyzing the chirp periodicity of the variation of propeller wake velocity under acceleration situation,this paper presents a time domain expression of the modulation envelope signal of the accelerating propeller noise,treating the signal as a Gaussian-shaped chirp periodic pulse train with increasing trend and fluctuating pulse amplitude.The paper investigates the characteristics of simplified fractional Fourier transform(SFRFT)spectrum of the chirp periodic signal,and thus obtains the relation between the chirp periodic signal and its chirp harmonics under the conditions of underwater passive detection.Furthermore,the experimental data of the cavitation tunnel satisfy the results obtained by simulation,which verifies the correctness of the proposed signal model.     

一种多径平坦衰落信道下的盲信噪比估计方法

提出一种多径平坦衰落信道下的盲信噪比估计方法.该算法首先利用数字通信信号的循环平稳统计特性构造接收信号的循环自相关矩阵,然后对该矩阵进行奇异值分解,由分解出的特征值信号子空间和噪声子空间,最后通过利用AIC信息准则分别估计信号子空间和噪声子空间的维数并最终估计出信道的平均信噪比.以MPSK信号为例进行了计算机仿真,结果表明了算法的有效性.     

A Perceptually Constrained GSVD-Based Approach for Enhancing Speech Corrupted by Colored Noise

The singular value decomposition (SVD)-based method for single-channel speech enhancement has been shown to be very useful when the additive noise is white. For colored noise, with this approach, one needs to whiten the noise spectrum prior to SVD-based approach and perform the inverse whitening processing afterwards. A truncated quotient SVD (QSVD)-based approach has been proposed to handle this problem and found very useful. In this paper, a generalized SVD (GSVD)-based subspace approach for speech enhancement is first extended from the concept of the truncated QSVD-based approach, in which the dimension of the signal subspace can be precisely and automatically determined for each frame of the noisy signal. But with this new approach some residual noise is still perceivable under lower signal-to-noise ratio conditions. Therefore a perceptually constrained GSVD (PCGSVD)-based approach is further proposed to incorporate the masking properties of human auditory system to make sure the undesired residual noise to be nearly un-perceivable. Closed-form solutions are obtained for both the GSVD- and PCGSVD-based enhancement approaches. Very carefully performed objective evaluations and subjective listening tests show that the PCGSVD-based approach proposed here can offer improved speech quality, intelligibility and recognition accuracy, whether the noise is stationary or nonstationary, especially when the additive noise is nonwhite