指数遗忘分布性能分析

该文分析讨论了指数遗忘变换(EFT)的信号瞬时频率表示性能。EFT采用单边指数窗口对信号进行采样和加权,因而可以利用迭代运算提高计算效率。与其他时频表示方法相比,EFT在处理较大长度的数据时具有优势,且更易于利用硬件实现。该文对EFT的瞬时频率表示偏差、均方差值的统计特性与信噪比及遗忘系数的关系进行了分析,给出了相应的仿真结果。同时为克服单边指数窗口的缺陷,提出了采用对称窗口的双边指数遗忘分布的计算形式,该方法在保持原有指数遗忘分布计算效率较高的优点的同时能够大大减小瞬时频率的表示偏差。     

基于指数遗忘分布的瞬时频率估计算法

本文提出了基于指数遗忘分布的信号瞬时频率估计算法.与其他的时频分布算法相比,通过采用单边指数分析窗口,指数遗忘分布可以在时频分析中引入迭代算法从而大大提高计算效率,本文对这一算法的性能进行了理论分析,同时其有效性在相应的仿真试验中得到了证明.     

基于时频分布迭代的跳频信号参数估计

提出了一种基于时频分布迭代的跳频信号参数估计新算法,利用时频平面最大值,通过计算跳频信号与最优原子时频分布的残差逐次迭代获取匹配于跳频信号分量的时频参数,进而实现跳频信号参数估计.理论分析和仿真结果表明,与基于匹配追踪和粒子群优化的跳频信号参数估计相比,基于时频分布迭代的参数估计算法在保证算法精度的情况下,有效地降低了算法的计算复杂度,为跳频信号盲接收的实时实现提供了一种新方法.     

基于FTFDS快速重建瞬时ISAR图像

时频分布级数(TFDS)在时频分辨率和交叉项干扰抑制间取得有效平衡,可准确重建复杂机动目标的瞬时ISAR图像.然而原始算法运算效率较低,虽有文献提出了快速计算思路,但难以直接工程应用.文中推导了可直接应用的二维插值滤波器实现形式,在此基础上提出FTFDS算法并从理论与实测两方面分析对比了运算量.分析表明FTFDS明显提升了运算效率,且算法适宜并行计算,时频分析可近实时的实现.仿真数据处理表明,该算法用于瞬时ISAR图像重建快速有效.     

基于窗口平均频率算法的调制信号识别

给出了一种对非平稳信号的时频特性进行快速分析的算法——移动窗口平均频率算法,通过移动窗口来截取信号,并计算窗口内信号平均频率来获得信号的时频分布,具有算法简单、运算速度快的特点;通过MATLAB仿真验证了该方法的有效性和可靠性;并给出了该算法的一个应用实例——从噪声中识别数字调制信号,能够对ASK、FSK、PSK等信号的调制类型进行识别,改善了信号识别的性能。     

一种基于SPWVD-WVD的高质量时频分析方法及ISAR成像应用

交叉项干扰抑制与高时频聚集度是准确反应信号的时频分布特征的重要因素。传统的魏格纳-维尔分布(Wigner-Ville Distribution,WVD)算法虽能获得较高的时频分辨特征,但分析多成分信号时存在严重的交叉项干扰问题,限制了其实用性;而平滑伪魏格纳-维尔分布(Smoothed Pseudo Wigner-Ville Distribution,SPWVD)算法虽在一定程度上抑制交叉项干扰,但降低了时频聚集度。为了解决上述问题,提出了基于SPWVD-WVD的时频分析方法。该方法利用SPWVD与WVD之间的滤波互消效应,将SPWVD二值化结果与WVD结果进行矩阵运算,最终得到高质量的时频分析结果。实验结果表明,所提出的算法能够有效去除多分量信号的交叉项干扰,提高信号分析结果的时频聚集度,还原多分量信号的真实时频分布。最后将该算法成功应用于逆合成孔径雷达成像中。     

基于时频分布的欠定混叠盲分离

针对欠定混合信号的盲分离问题,提出了基于时频分布的欠定盲分离算法,首先计算信号的时频分布矩阵并找出信号的自源时频点,然后把自源点对应的时频分布矩阵表示成三阶张量并通过张量分解估计出混合矩阵,最后通过计算矩阵的伪逆和时频合成来完成源信号的恢复.该算法不需要假设源信号是稀疏的或相互独立的.仿真结果表明与已有算法相比本文方法...     

应用光散射法测量雾化场粒子尺寸分布的研究

本文介绍一种测量油喷嘴雾化场粒径分布的光散射方法。它是利用雾化粒子对入射光的吸收和散射特性,通过测量前向三角度上的散射光能,从而反演出雾化粒子的尺寸分布。为了快速、准确地反演出雾化粒子分布,本文推导出一种用于该问题的迭代算法。另外,有迭代算法的程序设计中还引入了矩阵理论及并行运算的思想,这使得计算光散射强度函数的运算速度得到大大的提高。文中最高发表了有关该算法的数值模拟结果,以及实际测量超声波喷嘴所产生的燃油雾化粒尺寸分布的数据。     

基于时频分布的信号相位跳变检测与估计方法

本文提出了利用时频分布的信号相位跳变检测方法,对由相位跳变而引起的时频分布峰值的变化规律进行了讨论。分析表明信号时频表示的幅度能够直观地反映出其相位的变化,因此通过对时频分布峰值特征的检测可以准确定位频率跳变时刻,并能够定量的了解这些参数的变化,从而为信号的相位调制类型识别提供帮助。相应的仿真实验证明了这一分析结论。     

基于WD和时频滑窗掩膜的SAR干扰抑制算法

窄带干扰(NarrowBand Interference,NBI)和宽带干扰(WideBand Interference,WBI)的存在将会大幅度地降低合成孔径雷达(Synthetic Aperture Radar,SAR)图像的质量.本文在对NBI和WBI的时频分析基础上,提出了一种基于Wigner分布(Wigner Distribution,WD)和时频面滑窗掩膜技术的干扰抑制算法.该算法首先利用瞬时时刻重构序列与原序列之间的联系,提出了一种基于WD的高效信号重构算法.然后采用平滑伪WD分布(Smoothed Pseudo Wigner Distribution,SPWD)作为时频掩膜抑制WD的交叉项,并结合WD信号重构算法和时频平面滑窗掩膜技术提取并重构干扰信号,最后将重构的干扰信号在原始回波中对消实现干扰抑制.该算法能有效抑制SAR图像中的时变NBI和WBI,同时能够尽可能保留有用信号.仿真数据和实测数据结果分析验证了本文方法的有效性.     

基于熵的Gabor变换窗函数宽度自适应选择算法

该文针对Gabor变换中窗函数宽度选择的问题,提出了以提高Gabor表示的聚集性和时频分辨率为目的的窗函数宽度自适应选择算法。提出对香农熵的取值范围进行改进,使其更适合度量时频分布的聚集性,进而根据熵度量实现了与信号非平稳性相适应的最优窗函数宽度选择。仿真结果表明该算法对单分量及多分量信号都能有效地选择最优窗函数宽度,能够获得聚集性好、时频分辨率高的Gabor表示,并具有很好的抗噪性能。     

多分量信号快速时频分析方法

针对目前多分量时频分析方法计算复杂度高,难以实现持续信号实时处理的问题,提出一种多分量信号快速时频分析方法。该方法通过信号流逐窗时频变换,实现信号的快速时频分析,其主要计算过程为短时傅立叶变换,时间和空间复杂度低,易于工程实现。性能分析结果表明:该方法时频分辨率高,避免了交叉项干扰,能有效分析低信噪比信号。     

High Resolution Time-Frequency Distribution Based on Short-Time Sparse Representation

This paper focuses on the high resolution time-frequency distribution (TFD) of multicomponent signals with amplitude and frequency modulations, and a concise method named short-time sparse representation (STSR) is proposed. In STSR, both analysis and synthesis of the discrete signal can be achieved by exploiting the signal’s sparsity in frequency domain at each time instant. In order to fasten the STSR procedure, an efficient sparse recovery algorithm named SL0 is applied, and the signal model for each sliding window is modified to form the same dictionary, which guarantees that the whole recovery procedure adapts to the matrix form. The performance of STSR is compared with other TFD techniques and assessed in various configurations. It is shown that both preferable representation and acceptable computational cost can be obtained.     

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.     

块稀疏贝叶斯模型下的跳频信号时频分析

针对传统时频分析方法存在的时频聚集性差以及交叉项干扰的问题,本文将接收到的跳频信号进行分割,构建时频稀疏模型,利用模型中的统计特性和结构特性采用块稀疏贝叶斯学习算法对跳频信号的时频图进行重构,在不需知道稀疏度和噪声强度的情况下,得到了高精度的时频图。但是由于算法在高维参数空间进行参数估计时复杂度较高,本文采用近似替换的方法对该算法进行改进,将高维参数空间转换到原始参数空间计算,大大减少了算法的复杂度,仿真结果表明改进算法在低信噪比的情况下能有效的得到跳频信号的高精度时频图且复杂度大大降低。      

脉冲噪声环境中基于clipping方法的鲁棒LPFT及其重排算法

为了更好地处理脉冲噪声环境中的时变信号,本文提出了基于clipping方法的鲁棒局部多项式傅里叶变换(LPFT)及其重排算法。首先利用clipping方法对信号中掺杂的脉冲噪声进行抑制,得到较好的信号时频分布表示,然后将重排算法与该鲁棒LPFT相结合,以提高信号的时频聚集性。通过实验仿真可以看出,与基于中值滤波器的鲁棒LPFT相比,基于clipping方法的鲁棒LPFT同样能对被脉冲噪声干扰的信号给出较好的时频表示,而且其瞬时频率估计的最小均方误差(MSE)较低,计算量较小。并且,本文在基于clipping方法的鲁棒LPFT对掺杂脉冲噪声的信号进行处理的基础上,利用重排算法与其结合,有效增强了信号的时频聚集性。因此基于clipping方法的鲁棒LPFT及其重排算法是一种高效的处理脉冲噪声干扰信号及提高信号时频聚集性的方法。     

The Wigner distribution of noisy signals with adaptivetime-frequency varying window

Time-frequency representations using the Wigner distribution (WD) may be significantly obscured by the noise in the observations. The analysis performed for the WD of discrete-time noisy signals shows that this time-frequency representation can be optimized by the appropriate choice of the window length. However, the practical value of this analysis is not significant because the optimization requires knowledge of the bias, which depends on the unknown derivatives of the WD. A simple adaptive algorithm for the efficient time-frequency representation of noisy signals is developed in this paper. The algorithm uses only the noisy estimate of the WD and the analytical formula for the variance of this estimate. The quality of this adaptive algorithm is close to the one that could be achieved by the algorithm with the optimal window length, provided that the WD derivatives were known in advance. The proposed algorithm is based on the idea that has been developed in our previous work for the instantaneous frequency (IF) estimation. Here, a direct addressing to the WD itself, rather than to the instantaneous frequency, resulted in a time and frequency varying window length and showed that the assumption of small noise and bias is no longer necessary. A simplified version of the algorithm, using only two different window lengths, is presented. It is shown that the procedure developed for the adaptive window length selection can be generalized for application on multicomponent signals with any distribution from the Cohen (1989, 1990, 1992) class. Simulations show that the developed algorithms are efficient, even for a very low value of the signal-to-noise ratio     

Approximate Time-Variable Coherence Analysis of Multichannel Signals

We present a new method for signal extraction from noisy multichannel epileptic seizure onset EEG signals. These signals are non-stationary which makes time-invariant filtering unsuitable. The new method assumes a signal model and performs denoising by filtering the signal of each channel using a time-variable filter which is an estimate of the Wiener filter. The approximate Wiener filters are obtained using the time-frequency coherence functions between all channel pairs, and a fix-point algorithm. We estimate the coherence functions using the multiple window method, after which the fix-point algorithm is applied. Simulations indicate that this method improves upon its restriction to assumed stationary signals for realistically non-stationary data, in terms of mean square error, and we show that it can also be used for time-frequency representation of noisy multichannel signals. The method was applied to two epileptic seizure onset signals, and it turned out that the most informative output of the method are the filters themselves studied in the time-frequency domain. They seem to reveal hidden features of the epileptic signal which are otherwise invisible. This algorithm can be used as preprocessing for seizure onset EEG signals prior to time-frequency representation and manual or algorithmic pattern classification.     

Time-Frequency Atom Decomposition with Quantum-Inspired Evolutionary Algorithms

Time-frequency atom decomposition (TFAD) provides a flexible representation for non-stationary signals, but the extremely high computational effort greatly blocks its practical applications. Quantum-inspired evolutionary algorithms (QEA) are efficient optimization methods with strong search capability and rapid convergence. This paper proposes the application of a modified variant of QEA to the TFAD problem. The problem on TFAD with evolutionary algorithms is formulated. By using gray coding, elite groups, and an appropriate termination criterion, the modified QEA is developed to search the suboptimal time-frequency atoms from a very large and redundant time-frequency dictionary. Also, this paper discusses the reduction of the computational time in terms of parameter setting, and presents an application example of radar emitter signals. Extensive experiments show the effectiveness and practicability of the presented algorithm.     

Local polynomial modeling of time-varying autoregressive models with application to time-frequency analysis of event-related EEG

This paper proposes a new local polynomial modeling (LPM) method for identification of time-varying autoregressive (TVAR) models and applies it to time-frequency analysis (TFA) of event-related electroencephalogram (ER-EEG). The LPM method models the TVAR coefficients locally by polynomials and estimates the polynomial coefficients using weighted least-squares with a window having a certain bandwidth. A data-driven variable bandwidth selection method is developed to determine the optimal bandwidth that minimizes the mean squared error. The resultant time-varying power spectral density estimation of the signal is capable of achieving both high time resolution and high frequency resolution in the time-frequency domain, making it a powerful TFA technique for nonstationary biomedical signals like ER-EEG. Experimental results on synthesized signals and real EEG data show that the LPM method can achieve a more accurate and complete time-frequency representation of the signal.