基于高斯包络线性调频基自适应分解的ISAR成像

基于高斯包络线性调频基的自适应信号分解算法具有较高的分辨率,当被分析信号(如机动目标ISAR雷达回波信号)可以由调频类信号建模时具有超强分辨能力。该文在高斯包络线性调频基的自适应信号分解算法基础上,提出了基于优化初值选择高斯包络线性调频基自适应信号分解算法,并将其应用到ISAR成像中。仿真结果表明,和传统时频分析方法相比,该算法具有更加优良的性能,对机动目标进行瞬时成像时成像质量得到了较大幅度的改善。     

基于优化初值选择的自适应高斯包络线性调频基信号分解

基于高斯包络线性调频基的自适应信号分解是一种分辨力高、性能优良的时频分析算法,在语音信号、地震信号、雷达信号等可以用调频类函数进行建模的信号分析中有着广阔的应用前景。该算法在四维参数空间构造和求解超越方程,得到参数的闭式解,与传统的优化算法相比大大降低了运算量。对于由高斯包络线性调频基不相交或浅相交形成的简单信号,该算法具有非常优良的分辨性能,而对由高斯包络线性调频基深相交形成的复杂信号分解存在较大误差。本文针对这一问题进行研究,指出初值选择在该算法中的重要作用,分析了得到高精度分解结果的初值条件,提出了基于优化初值选择的高斯包络线性调频基自适应信号分解算法。通过在局部信号粗时频平面中搜索最优初值,结合自适应分解中建立和求解超越方程的方法得到参数闭式解,提高了分解精度,同时降低了运算量。对仿真信号及语音信号的分解效果验证了改进后算法的有效性和准确性。     

基于先验估计的自适应Chirplet信号展开

该文提出一种新的时频表示方法--自适应线性调频小波(Chirplet)信号展开算法。算法基于信号本征空间,融参数的初值估计和精确估计于一体,利用匹配追踪算法将信号自适应地展开在高斯线性调频小波基函数集上。通过展开系数和基函数参数获得信号的时频分布,其时频聚集性、抗噪性和时频分辨率不仅优于一般的时频分布而且优于已有的自适应时频分布,可以更好地刻画信号的本质。应用数值仿真检验了算法的有效性和时频分布的优良性能。     

自适应旋转投影分解法

本文提出种新的时－频分解方法－自适应旋转投影分解法。在表征信号空间的线性调频高斯信号集上，我们针对原始信号自适应地搜索出一组与信号匹配最好的基函数序，以此 用尽可能少的基函数来重构信号子空间。     

用于多分量线性调频信号的自适应核分布分析

该文针对多分量线性调频信号,提出了一种新的自适应核时频分布-自适应高斯核分布,并给出了有效的核函数估计准则;以自适应高斯核分布为例,分析了采用自适应核时频分布对信号自身项及交叉项的影响,从而说明自适应核相对于固定核的优势所在;总结了基于模糊域自适应设计多分量线性调频信号核函数的一般方法。计算机仿真结果表明了自适应高斯核分布在抑制交叉项并保持较高时频分辨力方面的有效性。     

基于时频子空间分解的宽带线性调频信号DOA估计

针对具有时变方向向量的宽带线性调频信号,该文建立了基于短时Wigner-Ville分布(WVD)的空间时频分布矩阵,通过对各个空间时频矩阵的特征分解获得对应的信号子空间和噪声子空间,给出了基于时频子空间投影实现多个时频点综合估计信号DOA的算法。利用空间时频分布的前后向平滑解决了具有相同时频特性信号的均匀线阵DOA估计问题。算法不需要聚汇和插值等复杂的矩阵变换,精度较高,计算简便.仿真实验显示该算法性能显著优越于基于矩阵插值的宽带调频信号DOA估计算法.     

自适应旋转投影分解法的快速实现

本文分析并提出了ＡＯＰ分解的快速算法。通过分析目标信号在线性调频高斯基上的投影极值特性，总结出一个最优搜索策略来搜索最佳高斯分解基函数。用此搜索策略来实现ＡＯＰ分解，其运算速度令人满意。     

ISAR机动目标的平动补偿和瞬时成像研究

机动目标成像近年来受到广泛注意.本文首先讨论平动补偿,它通常可以分解为两步进行──包络对齐和自聚焦,分析表明,针对平稳目标的包络对齐方法仍然适用于机动目标,而根据相干积累原理,已有的自聚焦方法从理论上和实际上都不是最优的,我们提出适用于机动目标和平稳目标的迭代相干积累自聚焦(ICSA)算法,PGA(相位梯度算法)是ICSA算法的一个特例; 然后,本文讨论机动目标的瞬时成像,它实际上是一个瞬时谱估计问题,已有的一些瞬时成像方法只适用于散射点子回波为线性调频信号(多普勒分布为直线).针对时频分布为非直线的情况,我们提出用自适应窗短时chirplet分解方法估计信号的瞬时频率和瞬时幅度,并结合"洁净"技术,提出了快速自适应窗短时chirplet分解成像(ACDI)算法.实测数据的处理表明本文提出ICSA算法和ACDI算法是有效的.     

一种有效的基于Chirplet自适应信号分解算法

基于线性调频小波(chirplet)的自适应信号分解法,将待分析的线性调频(Chirp)信号分解成为一组chirplet基函数的线性叠加,能够更清楚地表述Chirp信号的时频特征.其中关键的问题,是如何自适应地估计与信号最匹配的chirplet,这将影响到自适应分解的效果.目前,还没有一种有效chirplet估计算法.本文提出了一种新的chirplet估计算法,该法充分利用了chirplet的特点,具有较高的参数估计精度.仿真数据的实验结果证明了该方法的有效性.     

基于GSFrFT的STLFMCW信号参数估计

结合低截获概率雷达中常用的对称三角线性调频连续波信号(STLFMCW)的特点,提出了基于高斯短时分数阶傅里叶变换(GSFrFT)的STLFMCW信号参数估计算法.该算法首先在分数阶域二维时频面内搜索最佳旋转角,确定FrFT阶数,接着做信号的GSFrFT,然后在分数阶域表示出信号的时频关系,最后通过高斯窗长的调整实现信号参数的有效估计.仿真结果表明,该算法能在较低信噪比下精确估计信号参数.     

A fast refinement for adaptive Gaussian chirplet decomposition

The chirp function is one of the most fundamental functions in nature. Many natural events, for example, most signals encountered in seismology and the signals in radar systems, can be modeled as the superposition of short-lived chirp functions. Hence, the chirp-based signal representation, such as the Gaussian chirplet decomposition, has been an active research area in the field of signal processing. A main challenge of the Gaussian chirplet decomposition is that Gaussian chirplets do not form an orthogonal basis. A promising solution is to employ adaptive type signal decomposition schemes, such as the matching pursuit. The general underlying theory of the matching pursuit method has been well accepted, but the numerical implementation, in terms of computational speed and accuracy, of the adaptive Gaussian chirplet decomposition remains an open research topic. We present a fast refinement algorithm to search for optimal Gaussian chirplets. With a coarse dictionary, the resulting adaptive Gaussian chirplet decomposition is not only fast but is also more accurate than other known adaptive schemes. The effectiveness of the algorithm introduced is demonstrated by numerical simulations     

A four-parameter atomic decomposition of chirplets

A new four-parameter atomic decomposition of chirplets is developed for compact and precise representation of signals with chirp components. The four-parameter chirplet atom is obtained from the unit Gaussian function by successive applications of scaling, fractional Fourier transform (FRFT), and time-shift and frequency-shift operators. The application of the FRFT operator results in a rotation of the Wigner distribution of the Gaussian in the time-frequency plane by a specified angle. The decomposition is realized by using the matching pursuit algorithm. For this purpose, the four-parameter space is discretized to obtain a small but complete subset in the Hilbert space. A time-frequency distribution (TFD) is developed for clear and readable visualization of the signal components. It is observed that the chirplet decomposition and the related TFD provide more compact and precise representation of signal inner structures compared with the commonly used time-frequency representations     

Investigation of short-term changes in visual evoked potentials with windowed adaptive chirplet transform

We propose a new application of the adaptive chirplet transform that involves partitioning signals into non-overlapping sequential segments. From these segments, the local time-frequency structures of the signal are estimated by using a four-parameter chirplet decomposition. Entitled the windowed adaptive chirplet transform (windowed ACT), this approach is applied to the analysis of visual evoked potentials (VEPs). It can provide a unified and compact representation of VEPs from the transient buildup to the steady-state portion with less computational cost than its non-windowed counterpart. This paper also details a method to select the optimal window length for signal segmentation. This approach will be useful for long-term signal monitoring as well as for signal feature extraction and data compression.     

基于多尺度线性调频基信号稀疏分解的多分量LFM信号检测

该文针对传统的基于二次时频分析和原子追踪匹配方法处理多分量LFM信号时存在的时频干扰和等振幅交叉分解等问题,提出了一种基于多尺度线性调频基信号稀疏分解的多分量LFM信号检测方法,该方法采用多尺度的线调频基函数对多分量LFM信号进行投影分解,通过从不同的时间支撑区内投影系数最大的基函数中寻找出使分解信号能量最大的基元函数组合,逐次获得信号包含的能量最大的LFM信号分量,从基元函数连接形成的频率曲线即可获得LFM信号分量瞬时频率的估计,再对分量瞬时频率求起始时间点的频率值和曲线斜率便可得到该LFM分量的中心频率及调频斜率,仿真试验表明该文方法能精确地提取等振幅多分量LFM信号的瞬时频率,并具有很强的抗噪声干扰能力。     

利用自适应傅里叶分解的非平稳无线信道的时频表示

针对非平稳Clarke无线信道模型的时域冲激响应的性能分析需求,利用自适应傅里叶分解,引入了一种非平稳无线信道的时频表示方法和信道函数的重构表示,并给出了信道的单分量表示式、时间频率分布以及能量谱密度。在高速移动、快速时变环境下进行仿真,结果表明,本文提出的非平稳无线信道的表示方法能克服STFT、小波变换等相关方法的缺点,提高了无线信道时频表示的准确性,降低了信道的重构误差。      

The adaptive chirplet transform and visual evoked potentials

We propose a new approach based upon the adaptive chirplet transform (ACT) to characterize the time-dependent behavior of the visual evoked potential (VEP) from its initial transient portion (tVEP) to the steady-state portion (ssVEP). This approach employs a matching pursuit (MP) algorithm to estimate the chirplets and then a maximum-likelihood estimation (MLE) algorithm to refine the results. The ACT decomposes signals into Gaussian chirplet basis functions with four adjustable parameters, i.e., time-spread, chirp rate, time-center and frequency-center. In this paper, we show how these four parameters can be used to distinguish between the transient and the steady-state phase of the response. We also show that as few as three chirplets are required to represent a VEP response. Compared to decomposition with Gabor logons, a more compact representation can be achieved by using Gaussian chirplets. Finally, we argue that the adaptive chirplet spectrogram gives a superior visualization of VEP signals' time-frequency structures when compared to the conventional spectrogram.