基于STFT和FrFT的连续波雷达参数估计

连续波雷达的回波信号由目标进行调制,对回波参数的估计是雷达信号处理的主要任务.本文从短时傅里叶变换(STFT)和分数阶傅里叶变换(FrFT)的基本特点入手,介绍了他们进行雷达回波信号参数估计时的优缺点.针对这些特点,提出了一种基于两种方法的混合算法.混合算法分两步:首先,混合算法运用STFT进行速度粗测;然后,用FrFT对加速度进行估计同时进行速度的精测.最后本文进行蒙特卡罗仿真实验,实验结果证明,即使在较低信噪比下该算法依然有效.     

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。     

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.     

基于分数傅立叶变换的机载SAR多运动目标检测

机载合成孔径雷达（SAR）运动目标回波本质上为chirp信号,基于分数阶傅里叶变换（FRFT）的chirp基分解特性及分数阶傅立叶变换与时频分布的关系,给出了一种基于FRFT的针对强度相差较大的多运动目标检测方法。与传统的WVD方法相比,FRFT是一个线性变换,因此对于多个动目标就没有交叉项的干扰,简化了处理过程降低了处理复杂度。仿真结果验证了其有效性。     

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.     

基于分数阶自相关的线性调频信号的参数提取

线性调频(LFM)信号的参数包括瞬时频率、起始频率和初始相位.提出了一种基于分数自相关的线性调频信号的参数提取算法.首先该算法通过计算LMF信号在旋转角α的分数自相关以及相对应的p序的积分,这样当有确定的角度α时,分数阶傅里叶变换(FrFT)域中会出现一个峰值;然后根据这个峰值检测到信号,同时能够把参数提取出来;最后通过仿真验证该算法的有效性.     

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。     

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。     

基于支持向量机的小波包调制信号识别

基于小波包调制信号和OFDM信号在分数阶傅里叶变换域分布的不同,利用图像成形技术对接收信号的分数阶傅里叶变换域分布图进行处理,探讨了小波包调制信号和OFDM信号的分数阶域分布成形图的特征参数,利用支持向量机做分类器,实现了这两种多载波调制信号的分类识别。计算机仿真结果,验证了算法的性能。     

An approach for refocusing of ground fast-moving target and high-order motion parameter estimation using Radon-high-order time-chirp rate transform

Long synthetic aperture time can improve the imaging quality of a ground moving target, whereas a moving target may be severely smeared in the cross-range image due to the range migration and the Doppler frequency migration. In this paper, the effects of the third-order Doppler broadening and Doppler ambiguity of a fast-moving target are considered. To address these issues, a novel motion parameter estimation method named high-order time-chirp rate transform (HTRT) is proposed, and then a new synthetic aperture radar (SAR) imaging method based on Radon-HTRT (RHTRT) for a ground moving target is developed. The major contributions are as follows: 1) The proposed SAR imaging method can eliminate the Doppler ambiguity effect. 2) The proposed method can realize longer time coherent integration than Radon–Fourier transform (RFT) and Radon–fractional Fourier transform (RFRFT) methods. 3) The proposed method is computationally efficient since HTRT can obtain the motion parameters of a moving target via performing the 2-dimensional (2-D) fast Fourier transform (FFT). Both the simulated and real data processing results show that the proposed method can finely image a ground moving target in a high signal-to-clutter and noise ratio (SCNR) environment.     

斜视SAR分数阶距离多普勒成像算法

针对传统距离多普勒(RD)算法在斜视合成孔径雷达(SAR)成像时运算量不足和边缘存在干扰的问题,将传统的RD算法中的匹配滤波用分数阶Fourier变换替代,提出基于分数阶Fourier变换SAR斜视距离多普勒成像(FrRD)算法。理论分析斜视SAR回波信号分数阶Fourier变换域模型,徙动校正在距离分数阶Fourier域方位频域完成,给出FrRD算法仿真流程。仿真结果表明,与传统RD算法相比,FrRD算法成像副瓣更低,成像边界更加清晰,成像时间缩短近一半。     

Compressed sensing SAR imaging based on sparse representation in fractional Fourier domain

Compressed sensing(CS)is a new technique of utilizing a priori knowledge on sparsity of data in a certain domain for minimizing necessary number of measurements.Based on this idea,this paper proposes a novel synthetic aperture radar(SAR)imaging approach by exploiting sparseness of echo data in the fractional Fourier domain.The effectiveness and robustness of the approach are assessed by some numerical experiments under various noisy conditions and different measurement matrices.Experimental results have shown that,the obtained images by using the CS technique depend on measurement matrix and have higher output signal to noise ratio than traditional pulse compression technique.Finally simulated and real data are also processed and the achieved results show that the proposed approach is capable of reconstructing the image of targets and effectively suppressing noise.     

Cross-range scaling for non-uniformly rotating targets by sharpness maximization

Cross-range scaling is a crucial work to target recognition in inverse synthetic aperture radar (ISAR) systems. However, it is still challenging for maneuvering targets due to the time-varying Doppler history, which degrades the image as well. This paper presents a novel method to estimate the scaling factor by optimizing the image quality. One highlight of this work is the usage of Matching Fourier Transform (MFT), which coincides with the non-uniform rotation model or the variant Doppler. The backscattered reflectivity can be concentrated in MFT domain when the parametric transform is perfectly matched with the signal form. Thus another novelty lies in the sharpness maximization of the MFT image by iteratively compensating the range-dependent phase error joint with the image formation. The proposed scheme manages to output the scaling factor and enhanced images. To alleviate the influence of residual translation phase error on estimation, conventional autofocus methods are invoked during the iterations. Simulation and real-data results confirm the effectiveness of the proposed method.     

Detection,parameter estimation and imaging of maneuvering target in wide-band signal

The signal-to-noise ratio may be increased by the cross-range coherence integration so as to detect the moving target in low signal-to-noise ratio (SNR) condition. But, the radial velocity, acceleration and the change of acceleration due to the maneuvering motion of target may induce serious range migration and cross-range high-order phase terms leading to the unfocused cross-range image, the reduction of signal-noise ratio and the invalidation of target detection. Therefore, in order to solve these problems, this paper proposes a new method based on the adjacent correlation and scale transform methods for detection, parameters estimation and imaging of maneuvering targets in wide-band signal. This method can align the range and remove the cross-range high-order phase terms induced by the radial motion of target, enabling us to detect the target and estimate its moving parameters better. Finally, the simulated target is used to confirm that the method proposed by this paper can perfectly detect the maneuvering target in low signal-to-noise ratio condition, estimate its motion parameters and obtain an ISAR image of target. Supported by the National Natural Science Foundation of China (Grant No. 60502044), and the Program for New Century Excellent Talents in University (Grant No. NCET-06-0861)     

Inverse synthetic aperture radar phase adjustment and cross-range scaling based on sparsity

Due to inherent sparsity of ISAR images, compressive sensing theory has been used to obtain a high resolution image. However, before applying sparse recovery methods, the phase error due to the translational motion of target is compensated by autofocusing algorithms and the target rotation rate is estimated by cross-range scaling methods. In this paper, a comprehensive matrix model for a uniformly rotating target that includes the phase error and chirp-rate of the target is derived. Then by using sparsity and minimum entropy criterion, the estimation of residual phase error and the rotation rate is refined. In order to reduce the computational load, we simplify the model and by an iterative method based on adaptive dictionary, the phase error and chirp-rate are estimated separately. Actually, by exploiting a two-dimensional (2D) optimization method and the Nelder–Mead algorithm the phase adjustment is performed and the chirp-rate is estimated by solving a 1D optimization method for dominant range cells of the target. Finally, both simulation and practical data have been used to verify the validity of the proposed approach.     

LFM宽带雷达信号的多通道盲压缩感知模型研究

在传统压缩感知(Compressed sensing, CS)基础上,提出了一种基于盲压缩感知(Blind compressed sensing, BCS)理论的线性调频(Linear frequency modulated, LFM)雷达信号欠采样与重构的多通道模型.这一机制在稀疏基未知的条件下,利用LFM信号在分数阶傅里叶变换(Fractional Fourier transform, FRFT)域上良好的能量聚集特性,将多个LFM信号看作是在多个未知阶次下FRFT域的稀疏表达,通过时延相关解线调和逐次消去相结合的的欠采样方法逐一估计出每个通道的LFM信号满足聚集性条件的特定分数阶傅里叶域,以此构造出该通道LFM信号对应的DFRFT正交稀疏基字典,以各DFRFT 正交基为对角块构建混合信号正交稀疏基字典,最后利用块重构算法从测量值中估计出稀疏信号,同时验证了LF M信号多通道BCS问题解的唯一性,从而实现了稀疏基未知情况下针对多路LFM宽带雷达信号的多通道盲压缩感知.     

Research progress on discretization of fractional Fourier transform

As the fractional Fourier transform has attracted a considerable amount of attention in the area of optics and signal processing, the discretization of the fractional Fourier transform becomes vital for the application of the fractional Fourier transform. Since the discretization of the fractional Fourier transform cannot be obtained by directly sampling in time domain and the fractional Fourier domain, the discretization of the fractional Fourier transform has been investigated recently. A summary of discretizations of the fractional Fourier transform developed in the last nearly two decades is presented in this paper. The discretizations include sampling in the fractional Fourier domain, discrete-time fractional Fourier transform, fractional Fourier series, discrete fractional Fourier transform （including 3 main types： linear combination-type; sampling-type; and eigen decomposition-type）, and other discrete fractional signal transform. It is hoped to offer a doorstep for the readers who are interested in the fractional Fourier transform.     

分数阶Fourier域低分辨雷达飞机回波的分形特性分析与目标分类

常规低分辨雷达体制下的目标分类与辨识是雷达目标识别领域的一个研究难点。研究表明,地、海、空等雷达杂波具有分形特性,不同类型目标会对回波分形特性产生不同的影响,但在强杂波背景下,回波的分形特性更多地表现为杂波的特性。作为一种非平稳信号分析工具,分数阶Fourier变换可以有效地获取目标回波信号的细节特征并充分抑制杂波,且具有快速算法。为此,论文立足于分形及其相关理论,拟从分数阶Fourier域对常规雷达飞机目标回波的分形特性进行分析,估计和分析其分形参数,并对分数阶Fourier域回波分形特征在飞机目标分类中的应用进行探讨。研究结果表明,在最优变换阶数下,分数阶Fourier域飞机目标回波具有显著的分形特性,且充分反映了目标的特性,分形测度分析可以揭示回波的动力学演化机制,且最优变换域回波分形特征可以有效用于飞机目标的分类和识别。     

分数阶Fourier变换域极值搜索的混沌优化算法研究

正如傅里叶变换采用正弦基,单频信号能够在频域形成峰值,分数阶Fourier变换采用线性调频基,线性调频(LFM)信号能够在分数阶Fourier域上实现聚焦,利用此聚焦性通过搜索峰值可实现LFM信号检测和参数估计.通常采用步进式搜索方法,效率低下.为了克服该缺点,通过对分数阶Fourier域优化问题本质的研究,将混沌优化算法引入到分数阶Fourier域极值搜索中.仿真结果表明:本文的方法优于传统的步进式搜索法.