采用复数波数字滤波器实现希尔伯特(Hilbert)变换器

一个复数网络可以认为是由两个实网络所组成,它们分别传递复数信号的实部和虚部。若网络的阻抗是“一实”的,则能用波数字滤波器来实现。本文介绍用波数字格型滤波器来实现复数滤波器、其输出信号的虚部和实部之间满足希尔伯特(Hilbert)变换关系。由于波数字格型滤波器的衰减响应满足一定的对称性时,可使实现硬件减少,因此它为希尔伯特变换器的一种新的实现方法。     

基于Matlab的FIR型希尔伯特变换器设计

在通信系统中,希尔伯特变换是被广泛应用的重要变换。为了实现数字解调,通常需要借助希尔伯特变换器对信号进行分解,利用Matlab设计希尔伯特变换器是一种最为快捷、有效的方法。通过具体的设计、仿真及对原始信号和经过希尔伯特变换器输出延迟信号的比较,说明Matlab是一个在滤波器设计方面很有力的工具。     

多电平变换器——功率变换器新品种

多电平变换器是大功率应用优选功率变换器新品种时才引人注目的，本文阐述最新发展的三种多电平变换：（１）二极管箝位；（２）浮动电容器；（３）有各自直流源的级联逆变器，经计算机仿真，并用ＩＧＢＴ作功率开关器件和ＤＳＰ作全数字控制器的样机验证，给出了工作原理、特点，优缺点和应用范围。     

DSD/A数模变换器DSD-1700

ＤＳＤ－１７００是索尼公司与Ｂｕｒｒ－Ｂｒｏｗｎ公司合作开发的可在超级音频ＣＤ（ＳＡＣＤ）播放机中将数据流（ＤＳＤ）变换成模拟音频信号的新型Ｄ／Ａ变换器,本文介绍ＤＳＤ－１７００的工作原理、功能特点及基本应用。     

逐次逼近A／D变换器和D／A变换器

逐次逼近Ａ／Ｄ变换器和Ｄ／Ａ变换器洪志良（复旦大学电子工程系，上海，２００４３３）ｌ引言逐次逼近Ａ／Ｄ变换器是最早采用的Ａ／Ｄ变换技术，由于它对工艺技术的要求不苛刻，使用硬件少，早期就被用于较高精度Ａ／Ｄ变换器的集成中。逐次近似Ａ／Ｄ变换不用额外的工...     

希尔伯特变换器的新实现

利用波数字格型滤波器实现复数滤波器,其输出信号的实部和虚部之间满足希尔伯特变换关系。本文介绍这种变换器的新计算方法,它的公式和设计过程十分简单,以至用小的可编程计算器或微处理机就可完成必要的计算。     

A／D变换器系列讲座（三）梳状抽频滤波器和数字低通滤波器

Ａ／Ｄ变换器系列讲座（三）梳状抽频滤波器和数字低通滤波器洪志良（复旦大学，　电子工程系，上海，２００４３３）１引言上一讲我们介绍了过采样Ｄ变换是目前实现高精度Ａ／Ｄ变换器的重要手段［１～３］。由于过采样调制器的高密度数据不仅带有高频采样时钟的干扰，而...     

电源变换器及应用（续2）

电源变换器及应用（续２）西安通信学院王鸿麟陕西科技出版社张敏４、ＤＣ／ＤＣ变换器ＤＣ／ＤＣ变换器广泛用于直流电压的变换和分配。各种电子系统通常由输出一种直流电压的电源或电池组供电，这种电源的电压通常为５Ｖ、１２Ｖ、２４Ｖ、４８Ｖ等，而且这种电源的稳定...     

分数阶希尔伯特变换的复包络分析

分数Hilbert变换,也即传统的Hilbert变换由整数阶向分数阶的推广,由此其应用领域也得到了扩展,譬如在通信中的单边带调制解调系统,图像的边缘提取等领域中得到广泛的应用。首先,文中回顾了利用经典希尔伯特变换的正交性把经典的Hilbert变换的定义形式进行广义化后得到的分数阶Hilbert变换,并讨论了由分数阶Hilbert变换构造的两类复信号,最后讨论了这两类复信号的包络提取并且给出了具体的实例。     

复解析小波变换与语音信号包络提取和分析

利用传统的Ｈｉｌｂｅｒｔ变换方法提取语音信号包络存在一些固有缺陷,为此本文建议了一种复解析小波变换包络提取新方法。该方法将Ｈｉｌｂｅｒｔ分析与小波分析紧密地结合在一起。文中推导并论述了新包络滤波器的时域、频域构造条件和设计方法,选择Ｍｏｒｌｅｔ复小波对语音信号进行了初步数字仿真实验,结果证实了理论分析的正确性,并显示出文中提出的新的分析方法对于信号处理上有许多独特优势和潜在性能。     

Flexible Frequency-Band Reallocation: Complex Versus Real

This paper discusses a new approach for implementing flexible frequency-band reallocation (FFBR) networks for bentpipe satellite payloads which are based on variable oversampled complex-modulated filter banks (FBs). We consider two alternatives to process real signals using real input/output and complex input/output FFBR networks (or simply real and complex FFBR networks, respectively). It is shown that the real case has a lower overall number of processing units, i.e., adders and multipliers, compared to its complex counterpart. In addition, the real system eliminates the need for two Hilbert transformers, further reducing the arithmetic complexity. An analysis of the computational workload shows that the real case has a smaller rate of increase in the arithmetic complexity with respect to the prototype filter order and number of FB channels. This makes the real case suitable for systems with a large number of users. Furthermore, in the complex case, a high efficiency in FBR comes at the expense of high-order Hilbert transformers; thus, trade-offs are necessary. Finally, the performance of the two alternatives based on the error vector magnitude (EVM) for a 16-quadrature amplitude modulation (QAM) signal is presented.     

Blind equalization algorithm based on complex support vector regression

A new blind equalization algorithm for complex valued signals was proposed based on the framework of complex support vector regression(CSVR).In the proposed algorithm,the error function of multi-modulus algorithm (MMA) was substituted into CSVR to construct the cost function,and the regression relationship was established by widely linear estimation,and the equalizer coefficients were determined by the iterative re-weighted least square (IRWLS) method.Different from spliting the complex valued signals into real valued signals used in support vector regression,the Wirtinger’s calculus was used in complex support vector regression to analyze the complex signals directly in the complex regenerative kernel Hilbert space.Simulation experiments show that for QPSK modulated signals,compared with the blind equalization algorithm based on support vector regression,the equalization performance of the proposed algorithm is significantly improved in linear channel and nonlinear channel by choosing appropriate kernel function and iterative optimization method.     

Design of FIR Hilbert transformers using prescribed subfilters and nested FT technique

In this article, a transformation (subfilter) for designing finite impulse response (FIR) Hilbert transformers (HTs) is proposed. With our approach, except simple search procedures, neither optimisation nor any filter design algorithm is needed to obtain the transformation. The proposed subfilter requires only two multipliers regardless of the subfilter order. For the frequency transformation design, the purpose of the subfilter is to provide a “rough” shape of the desired HT; the two coefficients of the subfilter can be implemented as the form of sum of power of two (SOPOT) with only a few bits, thus leading to a multiplierless realisation. Moreover, by applying the transformation on the subfilter again, a technique named as nested frequency transformation (nested FT) is introduced. This technique can further reduce the number of multipliers needed in the overall HT.     

Construction of Hilbert Transform Pairs of Wavelet Bases and Gabor-Like Transforms

We propose a novel method for constructing Hilbert transform (HT) pairs of wavelet bases based on a fundamental approximation-theoretic characterization of scaling functions—the B-spline factorization theorem. In particular, starting from well-localized scaling functions, we construct HT pairs of biorthogonal wavelet bases of ${rm L}^2({BBR})$ by relating the corresponding wavelet filters via a discrete form of the continuous HT filter. As a concrete application of this methodology, we identify HT pairs of spline wavelets of a specific flavor, which are then combined to realize a family of complex wavelets that resemble the optimally-localized Gabor function for sufficiently large orders. Analytic wavelets, derived from the complexification of HT wavelet pairs, exhibit a one-sided spectrum. Based on the tensor-product of such analytic wavelets, and, in effect, by appropriately combining four separable biorthogonal wavelet bases of ${rm L}^2({BBR}^2)$, we then discuss a methodology for constructing 2-D directional-selective complex wavelets. In particular, analogous to the HT correspondence between the components of the 1-D counterpart, we relate the real and imaginary components of these complex wavelets using a multidimensional extension of the HT—the directional HT. Next, we construct a family of complex spline wavelets that resemble the directional Gabor functions proposed by Daugman. Finally, we present an efficient fast Fourier transform (FFT)-based filterbank algorithm for implementing the associated complex wavelet transform.      

多带通信号直接均匀欠采样技术

该文讨论了宽带数字接收机中对多个复或实的带通信号的直接均匀采样。对这多个通带位置及带宽均是任意的实或复的带通信号给出了采样率应满足的关系,用此采样率采样使输入数据得到有效的压缩,同时结合滤波器将频谱进行搬移,最后给出了实例。     

强干扰情况下用于CDMA系统的时空域联合处理

本文提出了一个时空域联合处理技术来对抗码分多址通信系统中的多址干扰.它由智能天线和多用户联合检测结合而成,智能天线可以压制与有用信号来向不同的干扰,余下的与用户信号来向相同的干扰可以被多用户联合检测系统消除.这种结构在干扰严重的信道仍能得到令人满意的效果.     

Approximation of complex IIR bandpass filters without arithmetic symmetry

Complex transfer functions are not restricted to having complex-conjugate symmetry in the frequency-domain, as is the case for real filters. This gives them more flexibility when they are used in communication systems with complex signals, such as intermediate frequency signals of wireless communication systems. This paper describes a set of algorithms and procedures that can be used in solving the approximation problem involved in deriving complex infinite-impulse-response bandpass transfer functions directly, without the requirement of first designing a real-transfer-function prototype filter, which is then frequency translated. Because the requirement for a real prototype filter is eliminated, the filters need not have arithmetic symmetry; this results in superior stopbands with smaller filter orders. The procedures can be used for both continuous and discrete-time filters, can allow for arbitrary stopband specifications, and can be used for either equi-ripple or monotonic passbands.     

On fast preprocessing schemes for the real‐valued spatially multiplexed MIMO detectors

Multiple‐input and multiple‐output detectors may rely on the complex and real signal models, yielding complex detectors for quadrature amplitude modulated signals and real detectors for pulse amplitude modulated signals, respectively. It is well‐known that the complex and real maximum likelihood detectors are equivalent. But relying on both the conventional real and pairwise real models, we show in this paper that some of the suboptimal real detectors are equivalent to their counterpart complex detectors, whereas some are not. The equivalence between the complex and pairwise real detectors also leads us to develop fast preprocessing algorithms for the real ordered successive interference cancelation and tree search detectors. Finally, we show that the preprocessing computations required by the equivalent suboptimal complex and pairwise real detectors are of the same complexity. When some practical preprocessing criteria are used, the real detectors may not detect the PAM signals in the pairwise manner. Such non‐pairwise real detectors outperform their counterpart complex detectors at the cost of higher preprocessing complexity. Copyright © 2012 John Wiley & Sons, Ltd.     

Frequency estimation of distorted non-stationary signals using complex H∞ filter

Mechanical vibration signals are always composed of harmonics of different order. A novel estimator is proposed for estimating the frequency of sinusoidal signals from measurements corrupted by White Gaussian noise with zero mean. Also low frequency sinusoidal signal is considered along with third and fifth order harmonics in presence of noise for estimating amplitudes and phases of different harmonics. The proposed estimator known as complex H_∞ filter is applied to a noisy sinusoidal signal model. State space modeling with two and three state variables is used for estimation of frequency in presence of white noise. Various comparisons in terms of simulation results for time varying frequency reveal that the proposed adaptive filter has significant improvement in noise rejection and estimation accuracy. Comparison in performance between two and three states modeling is presented in terms of mean square error (MSE) under different SNR conditions .The computer simulations clearly indicate that two states modeling based on Hilbert transform performs better than three states modeling under high noisy condition. Frequency estimation performance of the proposed filter is also being compared with extended complex Kalman filter (ECKF) under same noisy conditions through simulations.     

Orthogonal complex filter banks and wavelets: some properties anddesign

Previous wavelet research has primarily focused on real-valued wavelet bases. However, complex wavelet bases offer a number of potential advantageous properties. For example, it has been suggested that the complex Daubechies wavelet can be made symmetric. However, these papers always imply that if the complex basis has a symmetry property, then it must exhibit linear phase as well. In this paper, we prove that a linear-phase complex orthogonal wavelet does not exist. We study the implications of symmetry and linear phase for both complex and real-valued orthogonal wavelet bases. As a byproduct, we propose a method to obtain a complex orthogonal wavelet basis having the symmetry property and approximately linear phase. The numerical analysis of the phase response of various complex and real Daubechies wavelets is given. Both real and complex-symmetric orthogonal wavelet can only have symmetric amplitude spectra. It is often desired to have asymmetric amplitude spectra for processing general complex signals. Therefore, we propose a method to design general complex orthogonal perfect reconstruct filter banks (PRFBs) by a parameterization scheme. Design examples are given. It is shown that the amplitude spectra of the general complex conjugate quadrature filters (CQFs) can be asymmetric with respect the zero frequency. This method can be used to choose optimal complex orthogonal wavelet basis for processing complex signals such as in radar and sonar