Minimum variance filters and mixed spectrum estimation

This paper presents a spectral density estimator based on a normalized minimum variance (MV) estimator as the one proposed by Lagunas. With an equivalent frequency resolution, this new estimator preserves the amplitude estimation lost in Lagunas one. This proposition comes from a theoretical study of MV filters that highlights this amplitude lost. Two signal types are taken into account: periodic deterministic signals (narrow-band spectral structures) and stationary random signals (broad-band spectral structures). Without selecting a smoothing window, the proposed estimator is an alternative to Fourier-based estimator and, without modeling the signal, it is a concurrent to high-resolution estimators.     

Integration of Spatial–Spectral Information for Resolution Enhancement in Hyperspectral Images

In this paper, a new algorithm is proposed for resolution enhancement in hyperspectral images (HSIs). The key techniques are included: spectral unmixing and superresolution mapping, by which spatial and spectral information of HSIs is substantially fused. The proposed algorithm first represents each pixel in scene as a linear combination of landcover spectra and noise. Then, a fully constrained least squares algorithm is used to obtain the proportion of each landcover in each pixel, i.e., abundance, subjecting to two constraints: nonnegativity and sum-to-one. After that, superresolution mapping is performed on high-resolution grids according to spectral unmixing abundances of each landcover and following spatial correlation of clutters. Thus, by reasonably integrating spatial and spectral information of landcovers in HSIs, the proposed algorithm realizes resolution enhancement of the HSIs based on a back-propagation neural network. The proposed algorithm is independent from the a priori information associated with original HSIs, i.e., a main merit of the algorithm. In order to evaluate the performance of the new algorithm, numerical experiments are conducted on both simulated images and real HSIs collected by the Airborne Visible/Infrared Imaging Spectrometer. The proposed algorithm is compared with the traditional method in the experiments. The experimental results prove that the proposed algorithm effectively enhances the resolution of HSIs and indicate its applicability.     

Spectral estimation of video signals

Spectrum analyzers are ubiquitous in laboratory work concerning one dimensional signals. This is because linear operators are best examined in the frequency domain. Linear operators, such as linear filters, DCT coders, line shufflers, etc., dominate also the video systems scenario. Their frequency domain study is as appropriate and informative as it is in the case of their one-dimensional counterparts. This paper considers the problems associated with the introduction of two well-known spectral estimation techniques, the periodogram and AR estimates, to the context of television signals. The potential for application of spectral estimation to video problems is exemplified by a number of applications related to the fields of enhanced quality television and HDTV. Special attention is paid to the computational aspects, whose effective solution conditions the practical applicability of the proposed spectral estimation techniques.R. Rinaldo is currently at the Department of Electrical and Computer Engineering of the University of California, Berkeley.     

基于子空间分解的阵列流形向量估计新方法

实际阵列装配完成后的阵列流形向量与理论值存在偏差,这种偏差会导致阵列预设波束图的旁瓣升高、阵列高分辨算法的性能下降,严重影响阵列的实际应用。实际中先依据估计的部分实际阵列流形向量选取合适的误差模型,再根据模型得到逼近实际的阵列流形向量。现有的实际阵列流形向量估计方法有直接定义法和最小二乘法,这两种方法计算复杂度很高且估计精度随快拍数波动。对此本文给出一种新的阵列实际流形向量估计方法,它利用阵列接收数据协方差矩阵的信号子空间与阵列流形向量张成空间相同的特性来估计阵列的实际幅度相位响应,结合估计的波达方向,最后得到实际的阵列流形向量。仿真结果表明,本文所提方法比现有的两种估计方法估计精度高一倍且计算复杂度降低了一个数量级。      

Spectral estimation for the transmission line matrix method

The transmission line matrix (TLM) method for microwave circuit analysis calculates the time-domain variation of the electromagnetic fields in response to an arbitrarily chosen excitation. Spectral estimation for the TLM method by use of the discrete fourier transform and fast Fourier transform is reviewed. Error bounds are given and checked by means of a numerical example. A spectral estimation method based on Prony's method for use with TLM is presented. A numerical example shows that the method allows an order of magnitude reduction in the number of iterations in the TLM method for equal accuracy     

离散频谱梯形窗幅度比值校正法的频率估计

用FFT对正弦信号进行频率估计时,经常会遇到栅栏效应和频谱泄露的问题,为了提高正弦信号的频率估计精度,提出了一种离散频谱梯形窗幅度比值校正法。通过建立梯形窗的信号估计模型,并结合主瓣内最高谱线和次高谱线两条谱线的幅度比值关系,得到了加梯形窗的离散频谱校正公式。利用该算法对单频正弦信号的频谱校正误差进行了仿真,并且与加矩形窗时的频谱校正误差进行了比较。仿真结果表明,在信噪比较低时,该方法的频谱校正误差小,其性能明显优于加矩形窗的频谱校正精度。     

Estimation of wavefield power distribution in the remotely sensed environment: Bayesian maximum entropy approach

The problem of estimating, from one random realization of the remotely sensed signal, the spatial spectrum pattern (SSP) of the wavefield sources distributed in the environment is cast in the framework of Bayesian estimation theory. The kernel spectral estimation method that is familiar, for the classical SSP estimation problem, with the Fourier transform operator and white noise in the observations is extended to incorporate spatial correlation in the data, the system-oriented model of the signal formation operator, and the maximum entropy (ME) statistical a priori information about the SSP. To derive the estimate of the SSP, we applied the Bayesian strategy for maximization of the a posteriori probability density function of the randomized ME model of the SSP. The estimator was obtained as a nonlinear adaptive algorithm that also permits a concise robust implementation. The optimal algorithm implies formation of the second-order sufficient statistics of the data and their smoothing by applying the window operator. The new formalism of the sufficient statistics and windows, explaining their adjustment to the metrics in a solution space, a priori nonparametric model and assumed correlation properties of the desired SSP, is developed. Simulation results are included to illustrate the overall performance of the proposed method in an example of application to radar image formation.     

基于短时谱分析的语音增强改进算法

介绍了基本的语音增强短时谱分析算法的原理.基于经典的谱减算法进行改进,引入了先验信噪比估计,以及时间回归平均法对噪声进行更新,以及一系列后处理改进方法,提高了降噪效果.在保持语音可懂度的基础上,增强了噪声衰减量.主、客观实验结果表明,针对汽车噪声一类的平稳噪声,算法效果出色;而对于街道、人声一类非稳态噪声,该算法性能优...     

基于多正弦窗谱估计的改进谱减法语音增强

采用运算简单、实时性好、改进的功率谱减法对带噪语音进行增强。鉴于谱减法的特点,文中采用了多正弦窗谱估计对功率谱进行估计。多正弦窗谱估计是一种非参数的现代谱估计方法,与周期图法相比,谱估计偏差和方差更小,可以得到效果更好的增强语音。分别对周期图法、多窗谱法及多正弦窗谱法的增强效果进行了比较。实验结果表明,语音在多正弦窗谱法增强后,分段信噪比最高,且没有因为谱减产生的音乐噪声,主观听觉效果最好。多正弦窗谱估计对输入信噪比较低的含噪语音增强效果非常好。     

基于部分平行ADMM求解谱寻求问题的时频分析

经典的非参数谱分析方法使用滑动窗口来捕捉大多数时间序列的频谱特性,然而这种方法不能很好地应用在时间序列的时频谱是时间连续的信号上.对于一些其时频谱满足时间连续频率稀疏的非平稳信号,提出了一种利用部分平行交替方向乘子法（Alternating Direction Method of Multipliers,ADMM）求解谱寻求问题用于此类信号的时频分析方法.对一段加噪声的仿真信号和一段EEG（脑电）信号使用提出的方法进行时频分析.仿真结果表明：与短时傅里叶的分析方法相比,提出的方法不仅提高了时频谱的频率分辨率和时间分辨率,还有效抑制了噪声.最后从ADMM算法停止准则的角度说明了算法的收敛.     

Spectral decomposition in multichannel recordings based onmultivariate parametric identification

A method of spectral decomposition in multichannel recordings is proposed, which represents the results of multivariate (MV) parametric identification in terms of classification and quantification of different oscillating mechanisms. For this purpose, a class of MV dynamic adjustment (MDA) models in which a MV autoregressive (MAR) network of causal interactions is fed by uncorrelated autoregressive (AR) processes is defined. Poles relevant to the MAR network closed-loop interactions (cl-poles) and poles relevant to each AR input are disentangled and accordingly classified. The autospectrum of each channel can be divided into partial spectra each relevant to an input. Each partial spectrum is affected by the cl-poles and by the poles of the corresponding input; consequently, it is decomposed into the relevant components by means of the residual method. Therefore, different oscillating mechanisms, even at similar frequencies, are classified by different poles and quantified by the corresponding components. The structure of MDA models is quite flexible and can be adapted to various sets of available signals and a priori hypotheses about the existing interactions; a graphical layout is proposed that emphasizes the oscillation sources and the corresponding closed-loop interactions. Application examples relevant to cardiovascular variability are briefly illustrated     

一种快速的谱分析迭代法

为克服现有正弦波频率估计算法运算量大或估计精度不高的弊端,提出了一种快速的频率估计算法,该算法利用正弦波频谱对称性原理和频移技术,使信号频率位于峰值点附近再进行估计。仿真结果表明:文中算法稳定性不随被估计频率分布而产生波动,在低信噪比条件下不存在发散现象,以它为初始值进行一次谱分析迭代,其均方根误差在整个频段都接近克拉美-罗限。     

Fast Characterization of Dispersion and Dispersion Slope of Optical Fiber Links Using Spectral Interferometry With Frequency Combs

We demonstrate fast characterization ( $sim$1.4 $mu hbox{s}$) of both the dispersion and dispersion slope of long optical fiber links ( $sim$25 km) using dual quadrature spectral interferometry with an optical frequency comb. Compared to previous spectral interferometry experiments limited to fiber lengths of meters, the long coherence length and the periodic delay properties of frequency combs, coupled with fast data acquisition, enable spectral interferometric characterization of fibers longer by several orders of magnitude. We expect that our method will be useful to recently proposed lightwave techniques like coherent wavelength-division multiplexing and to coherent modulation formats by providing a real-time monitoring capability for the link dispersion. Another area of application would be in stabilization of systems which perform frequency and timing distribution over long fiber links using stabilized optical frequency combs.      

All-Pole Estimation in Spectral Domain

Autoregressive (AR) modeling is a popular spectral analysis method commonly resolved in the time domain. This paper presents a novel AR analysis framework dealing with the estimation of poles directly from spectral samples. The basis of the method lies on a minimizing functional built with a certain mapping of the spectral residue. The optimization mechanism is based on the multivariate Newton-Raphson algorithm. Two different mappings are considered, namely, linear and logarithmic. The linear case results in a nonquadratic convex functional, whose global minimum is equivalent to that of the time-domain autocorrelation method. The logarithmic case under the maximum likelihood criterion turns out equivalent to the Whittle likelihood, proven here to be suitable for frequency selective estimation. The statistical and convergence performance of the method is demonstrated with simulations on stochastic and deterministic harmonic signals.     

基于H.264视频编码的运动估计算法优化

 运动估计是视频压缩中最重要的环节.H.264编码器由于采用了高精度运动矢量,计算量迅速增长,运动估计消耗整个编码时间80％左右.本文在分析UMHexagonS算法的基础上,分别对UMHexagonS算法中搜索窗口大小的选择、大六边形搜索和小六边形(小钻石)搜索模式三方面做了优化,在保持了原有图象质量的情况下有效的节省了运动估计时间.通过对各种测试序列的实验证明,优化后的算法与UMHexagonS算法相比,在重建图象质量和码率接近的情况下,运动估计时间平均节省了18.292%,降低了算法的复杂度,提高了编码器的实时性.     

Spectral Analysis of Signals [Book Review]

This book is a revised and expanded version of Introduction to Spectral Analysis by the same authors, a popular text and reference on the subject since its publication in 1997. The updated book consists of six chapters and four appendices. The main text of each chapter discusses the principles and fundamentals, while the complements included at the end of each chapter examine more advanced topics. Much of the background material need for the book, such as linear algebra, Cramer-Rao bound analysis, and model order selection, is provided in the appendices. Some of the topics covered include: Fourier-based nonparametric methods; parametric methods for rational spectra; parametric methods for line spectra; the filter-bank approach; and beamforming and direction-of-arrival estimation problems. This timely book is suitable for senior undergraduate and graduate students as well as engineers and other professionals. The material can easily fit in a one-semester spectral analysis course.     

Generalized IHS‐Based Satellite Imagery Fusion Using Spectral Response Functions

Image fusion is a technical method to integrate the spatial details of the high‐resolution panchromatic (HRP) image and the spectral information of low‐resolution multispectral (LRM) images to produce high‐resolution multispectral images. The most important point in image fusion is enhancing the spatial details of the HRP image and simultaneously maintaining the spectral information of the LRM images. This implies that the physical characteristics of a satellite sensor should be considered in the fusion process. Also, to fuse massive satellite images, the fusion method should have low computation costs. In this paper, we propose a fast and efficient satellite image fusion method. The proposed method uses the spectral response functions of a satellite sensor; thus, it rationally reflects the physical characteristics of the satellite sensor to the fused image. As a result, the proposed method provides high‐quality fused images in terms of spectral and spatial evaluations. The experimental results of IKONOS images indicate that the proposed method outperforms the intensity‐hue‐saturation and wavelet‐based methods.     

Image Fusion Processing for IKONOS 1-m Color Imagery

Many image fusion techniques have been developed. However, most existing fusion processes produce color distortion in 1-m fused IKONOS images due to nonsymmetrical spectral responses of IKONOS imagery. Here, we proposed a fusion process to minimize this spectral distortion in IKONOS 1-m color images. The 1-m fused image is produced from a 4-m multispectral (MS) and 1-m panchromatic (PAN) image, maintaining the relations of spectral responses between PAN and each band of the MS images. To obtain this relation, four spectral weighting parameters are added with the pixel value of each band of the original MS image. Then, each pixel value is updated using a steepest descent method to reflect the maximum spectral response on the fused image. Comparison among the proposed technique and existing processes [intensity hue saturation (IHS) image fusion, Brovey transform, principal component analysis, fast IHS image fusion] has been done. Our proposed technique has succeeded to generate 1-m fused images where spectral distortion has been reduced significantly, although some block distortions appeared at the edge of the fused images. To remove this block distortion, we also proposed a sharpening process using a wavelet transform, which removed block distortion without significant change in the color of the entire image.     

Digital signal processing 2. Spectral analysis

For pt.1 see ibid., vol.5, no.1, p.13 (1993). The subject of the paper is the estimation of the spectra of signals and both classical estimation methods and modern model-based methods are discussed. These techniques are compared and results are presented which demonstrate the superior resolution which is obtained with modern autoregressive spectral analysis methods. Examples are given of the application of these techniques in the areas of speech processing and biomedical signal analysis