共查询到16条相似文献,搜索用时 453 毫秒
1.
2.
作为一种参数化时频分析的方法,基于高斯包络线性调频基自适应信号分解的快速算法具有分辨力高、零交叉项和计算量小的优点,在信号时频分析中具有独特的优势和广阔的应用前景.然而该快速算法却存在由于采样点初值选择不当而造成分解失效的缺点,虽然后来的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对初值选择算法进行了改进,提高了分解性能的稳定性,但仍存在较多的问题没有解决.本文将对这些问题进行研究和改进,并提出短时自适应高斯包络线性调频基信号分解算法.算法通过加短时窗来增强时频中心定位的准确性,通过控制采样基时宽来获取有效的初始方差取值范围,从而提高了分解的自适应性和稳定性.对仿真信号和语音信号的分解结果表明了该算法的有效性. 相似文献
3.
4.
一种有效的基于Chirplet自适应信号分解算法 总被引:16,自引:2,他引:14
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. 相似文献
5.
FM^2let变换分解力图采用最少的线性或非线性调频基函数来分解信号。FM^2let变换分解算法的基本原理是利用信号的瞬时频率求得FM^2let原子的5个参数,然后基于自适应匹配投影塔形分解思想实现FM^2let变换的分解。FM^2let变换分解算法在信号分析处理方面具有运算量小、运算速度快等优良的性能。FM^2let谱分辨率高、抗噪性能好、参数数据量少。并且利用伪时频分布可以去除交叉项干扰。理论和实验证明,FM^2let变换分解算法能对不同的信号结构表现出很强的适应性,能够简洁地表征信号时变或时不变特性。 相似文献
6.
7.
为克服时频关系为线性的基函数的不足,该文提出一种新的信号分解算法修正自适应Chirplet分解法,将Chirplet基函数推广到三次相位信号的形式,因此可以逼近信号中的非线性时变结构成分。同时提出了一种快速分解算法,该算法通过计算信号的三次相位函数,可得到其能量分布集中于信号的瞬时频率变化率曲线上的结论,此时通过谱峰检测可同时获得基函数的二、三次相位系数,时间中心以及幅度的估计;进而通过解调频技术获得其初始频率和时间宽度的估计。文中给出了实现该方法的具体步骤,并分别以仿真信号和蝙蝠回声定位信号为例验证了该算法的有效性。 相似文献
8.
自适应旋转投影分解法 总被引:37,自引:3,他引:34
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。 相似文献
9.
基于局部解线性调频的多频连续波雷达实时加速度补偿算法 总被引:5,自引:0,他引:5
本文给出了多频连续波雷达的一种实时加速度补偿算法。带有加速度信息的雷达回波相当于线性调频信号,目前各种解线性调频算法的最主要缺点在于运算量大,难以用于实时处理。本文提出的算法通过等效的复调制、低通、抽取等处理,得到了完全包含加速度信息的局部时域信号,然后利用加速度模板的方法进行了局部的解线性调频,从而实现加速度补偿。这种方法具有较高的估计精度,并且大大降低了解线性调频的运算量,可以用于雷达实时信号处理。仿真结果证明了该方法的有效性。 相似文献
10.
11.
A fast refinement for adaptive Gaussian chirplet decomposition 总被引:10,自引:0,他引:10
Qinye Yin Shie Qian Aigang Feng 《Signal Processing, IEEE Transactions on》2002,50(6):1298-1306
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 相似文献
12.
The adaptive chirplet transform and visual evoked potentials 总被引:2,自引:0,他引:2
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. 相似文献
13.
A four-parameter atomic decomposition of chirplets 总被引:12,自引:0,他引:12
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 相似文献
14.
A CLOSED-FORM WIDEBAND DIRECTION-OF-ARRIVAL ESTIMATION WITH CHIRPLET-BASED ADAPTIVE SIGNAL DECOMPOSITION ALGORITHM 总被引:1,自引:0,他引:1
Feng Aigang Yin Qinye Wu Xiaojun Zhao Zheng 《电子科学学刊(英文版)》2003,20(1):1-7
The Direction-Of-Arrival (DOA) estimation with Coherent Signal Subspace (CSS)is not easy to cxtend from narrowband to wideband case.Time-frequency analysis is a powerfultechnique to deal with time-variant or non-stationary signal. Its combination with CSS exploresa new field in signal processing, especially the wideband DOA estimation. The chirp function isone of the most fundamental functions in nature. Many nature events can be modeled as chirpletfunction, such as radar system or scismic exploring.Hence,the chirplet-based signal decomposi- 相似文献
15.
基于语音信号在离散余弦域上的近似稀疏性,针对采用随机高斯观测矩阵及线性规划方法进行语音压缩感知与重构时,重构零(近似零)系数定位能力差而导致重构效果不好的缺点,本文提出一种新的行阶梯矩阵做观测矩阵,用对偶仿射尺度内点重构算法对语音进行压缩感知与重构,并对该算法下的重构性能进行理论分析.语音压缩感知仿真结果表明,在离散余弦基下,压缩比(观测序列与原始序列样值数之比)为1∶4时,行阶梯观测矩阵下的平均重构信噪比比随机高斯观测矩阵下提高9.73dB,平均MOS分比随机高斯观测矩阵下提高1.22分. 相似文献
16.
本文提出了一种基于数据驱动字典和过完备稀疏表示的自适应语音增强方法。首先在训练阶段采用干净语音基于K奇异值分解(K singular value decomposition, K SVD)算法训练过完备字典,然后在测试阶段根据含噪语音的噪声方差自适应选择最优的阈值,采用正交匹配追踪算法对含噪语音信号在过完备字典上进行稀疏分解,最后利用系数稀疏表示重构语音信号,从而达到语音增强的目。该方法不像传统语音增强方法那样减少或消去噪声,而是从字典中选取适当的原子表示纯净信号,从而把纯净信号从含噪信号中分离出来。对白噪声和有色噪声环境下重构语音进行了主客观评价。仿真结果显示:该方法能有效去除加性噪声,并且改善了语音质量。 相似文献