基于小波和最小绝对误差的去噪抗扰动辨识方法

噪声和扰动是影响系统辨识的两个不利因素,而实际系统不可避免地受到噪声的污染和瞬时扰动的影响.本文采用不同的小波函数和不同的阈值处理方法,对噪声污染的系统输出进行多次小波分解、去噪和重构,从而达到削减噪声的目的.对于去噪后的数据,由于包含瞬时扰动,利用最小二乘辨识算法仍然不能获得满意的结果.为此,在小波去噪的基础上,提出了一种最小绝对误差(LA)辨识算法.仿真研究表明,本文提出的方法能够同时克服噪声和瞬时扰动的不利影响,获得好的辨识结果.     

Noise in RF-CMOS mixers: a simple physical model

Flicker noise in the mixer of a zero- or low-intermediate frequency (IF) wireless receiver can compromise overall receiver sensitivity. A qualitative physical model has been developed to explain the mechanisms responsible for flicker noise in mixers. The model simply explains how frequency translations take place within a mixer. Although developed to explain flicker noise, the model predicts white noise as well. Simple equations are derived to estimate the flicker and white noise at the output of a switching active mixer. Measurements and simulations validate the accuracy of the predictions, and the dependence of mixer noise on local oscillator (LO) amplitude and other circuit parameters     

偏差补偿比例互相关熵算法

为了解决输入信号含有噪声和非高斯输出噪声的稀疏系统辨识问题,本文提出一种偏差补偿比例更新互相关熵算法。基于互相关熵的自适应滤波算法可以消除非高斯噪声的影响, 进一步应用无偏准则来解决含噪输入信号带来的估计偏差问题。另外,将比例更新机制引入算法,通过自适应调节步长参数以增强算法的跟踪性能。仿真结果表明所提算法对于输入信号受噪声干扰和非高斯输出噪声环境下的稀疏系统辨识问题具有强的鲁棒性和稳态性能。      

互耦补偿后自适应阵列性能的分析

对互耦补偿后的自适应阵列性能作了深入的分析。分析表明,通过对互耦的补偿,自适应阵列能够完全抑制干扰,但是噪声的输出功率随着波束扫描方向的变化而发生变化,导致阵列输出信干噪比（SINR）性能下降。因此,互耦补偿后噪声是影响自适应阵列输出SINR性能的主要因素。仿真结果进一步验证了结论的正确性。     

The effect of varactor nonlinearity on the phase noise ofcompletely integrated VCOs

This work discusses variations in phase noise over the tuning range of a completely integrated 1.9-GHz differential voltage-controlled oscillator (VCO) fabricated in a 0.5-μm bipolar process with 25-GHz f _t. The design had a phase noise of -103 dBc/Hz at 100 kHz offset at the top of the tuning range, but the noise performance degraded to -96 dBc/Hz at 100 kHz at the bottom of the tuning range. It was determined that nonlinearities of the on-chip varactors, which led to excessively high VCO gain at the bottom of the tuning range, were primarily responsible for this degradation in performance. The VCO has a power output of -5 dBm per side. Calculations predict phase noise with only a small error and provide design insight for minimizing this effect. The oscillator core drew 6.4 mA and the output buffer circuitry drew 6 mA, both from a 3.3-V supply     

一种新的间歇混沌信号Poincaré映像判别方法

针对混沌振子微弱信号检测中间歇混沌信号的判别问题,该文分析了噪声对Poincaré截面的扰动影响,提出一种基于Poincaré映像的新方法,并通过数值仿真对该方法进行了验证,结果表明在强噪声作用下,即使相空间分量输出波形难以进行判别,该方法仍然能够实现间歇混沌发生频率的有效判别,且抑制了混沌振子自发的短时间周期振荡现象,实现了强噪声背景下微弱周期信号的快速有效检测.     

Approach based on combination of vector neural networks for emitter identification

To deal with the problem of emitter identification caused by the measurement uncertainty of emitter feature parameters, this study proposes a new identification algorithm based on combination of vector neural networks (CVNN), which is deduced from the backpropagation vector neural network and can realise the nonlinear mapping between the interval-value input data and the interval-value output emitter types. The key idea of CVNN is to adopt a combination of multiple multi-input/single-output neural networks to construct an identification system; each of the networks can only realise the identification function between two emitter types. Through quantitative analysis, it can be concluded that the proposed algorithm requires less computational load in the training stage. A number of simulations are presented to demonstrate the identification capability of the CVNN algorithm for emitter signals with and without additive noise. Simulation results show that the proposed algorithm not only has better identification capability, but also is relatively more insensitive to noise.     

A blind multichannel identification algorithm robust to orderoverestimation

Active research in blind single input multiple output (SIMO) channel identification has led to a variety of second-order statistics-based algorithms, particularly the subspace (SS) and the linear prediction (LP) approaches. The SS algorithm shows good performance when the channel output is corrupted by noise and available for a finite time duration. However, its performance is subject to exact knowledge of the channel order, which is not guaranteed by current order detection techniques. On the other hand, the linear prediction algorithm is sensitive to observation noise, whereas its robustness to channel order overestimation is not always verified when the channel statistics are estimated. We propose a new second-order statistics-based blind channel identification algorithm that is truly robust to channel order overestimation, i.e., it is able to accurately estimate the channel impulse response from a finite number of noisy channel measurements when the assumed order is arbitrarily greater than the exact channel order. Another interesting feature is that the identification performance can be enhanced by increasing a certain smoothing factor. Moreover, the proposed algorithm proves to clearly outperform the LP algorithm. These facts are justified theoretically and verified through simulations     

Higher-order statistics-based input/output system identification and application to noise cancellation

Higher-than-second-order statistics-based input/output identification algorithms are proposed for linear and nonlinear system identification. The higher-than-second-order cumulant-based linear identification algorithm is shown to be insensitive to contamination of the input data by a general class of noise including additive Gaussian noise of unknown covariance, unlike its second-order counterpart. The nonlinear identification is at least as optimal as any linear identification scheme. Recursive-least-squares-type algorithms are derived for linear/nonlinear adaptive identification. As applications, the problems of adaptive noise cancellation and time-delay estimation are discussed and simulated. Consistency of the adaptive estimator is shown. Simulations are performed and compared with the second-order design.Part of the results of this paper were presented at the workshop on HOSA, Vail, CO, June 1989, and at the International Conference on ASSP, Albuquerque, NM, April 1990. The work of G. B. Giannakis in this paper was supported by LabCom Contract 5-25254.     

半导体激光器光输出噪声测量及与电噪声的相关性

采用互谱估计方法测量了半导体激光器的微弱电噪声谱及光噪声谱。结果表明两者在超辐射区有较强的相关性。对光噪声的形成机理分析证明由载流子起伏形成的光噪声与电噪声完全相关，而由外量子效率起伏引起的光噪声与电噪声是不相关的。     

Transmitter identification using embedded pseudo random sequences

A transmitter identification system for DTV distributed transmission network using embedded pseudo random sequences is investigated. Different orthogonal pseudo random sequences and their suitability for transmitter identification are discussed. Code generators are developed to study the auto-correlation and cross-correlation properties of the Kasami sequences. To speed up the identification process, the embedded pseudo random sequence is preferred to be time-synchronized with the DTV frame structure. Therefore, the length of the identification code has to be truncated before it is fitted into each field of the ATSC DTV signal. The impact of truncation noise and in-band DTV interference on transmitter identification is also investigated. It is shown that the auto-correlation and cross-correlation properties are only slightly affected by truncation. It is also found that the dominant interference to the transmitter identification is the in-band DTV signal. The signal to truncation noise ratio and signal to DTV interference ratio in the correlation output are derived, and verified via simulation. It is further recognized that in-band DTV interference can only be mitigated by increasing the code length or by time-domain averaging technique to smoothen out the in-band interference.     

Blind Maximum-Likelihood Identification of Wiener Systems

This paper is about the identification of discrete-time Wiener systems from output measurements only (blind identification). Assuming that the unobserved input is white Gaussian noise, that the static nonlinearity is invertible, and that the output is observed without errors, a Gaussian maximum-likelihood estimator is constructed. Its asymptotic properties are analyzed and the Cramer-Rao lower bound is calculated. A two-step procedure for generating high-quality initial estimates is presented as well. The paper includes the illustration of the method on a simulation example.     

Blind identification of an autoregressive system using a nonlineardynamical approach

The problem of identifying an autoregressive (AR) system with arbitrary driven noise is considered here. Using an abstract dynamical system to represent both chaotic and stochastic processes in a unified framework, a dynamic-based complexity measure called phase space volume (PSV), which has its origins in chaos theory, can be applied to identify an AR model in chaotic as well as stochastic noise environments. It is shown that the PSV of the output signal of an inverse filter applied to identify an AR model is always larger than the PSV of the input signal of the AR model. Therefore, by minimizing the PSV of the inverse filter output, one can estimate the coefficients and the order of the AR system. A major advantage of this minimum-phase space volume (MPSV) identification technique is that it works like a universal estimator that does not require precise statistical information about the AR input signal. Because the theoretical PSV is so difficult to compute, two approximations of PSV are also considered: the e-PSV and nearest neighbor PSV. Both approximations are shown to approach the ideal PSV asymptotically. The identification performance based on these two approximations are evaluated using Monte Carlo simulations. Both approximations are found to generate relatively good results in identifying an AR system in various noise environments, including chaotic, non-Gaussian, and colored noise     

MEMS陀螺仪随机误差的动态辨识

针对经典Allan方差在分析和定量描述微机电系统(MEMS)陀螺仪噪声项时存在的问题,提出将动态Allan方差用于MEMS陀螺仪输出信号分析并加以改进。根据Allan方差的原理,实现误差项系数的动态辨识,得到各类型误差随时间的变化规律。普通最小二乘法求解误差系数时存在个别为负的问题,因此,将非线性最优化的单纯形法用于方差曲线的拟合。实验结果表明,改进后的方法不仅能准确描述数据的噪声量值,还能反映信号的频率稳定性和误差项的变化特征。     

一种适用于椒盐-高斯干扰信号的自适应滤波改进算法

图像通信由于成像设备自身特点和通信过程中的光-电转换机制,一般含有椒盐-高斯干扰信号,信号交叉影响会导致单一的滤波方法效果不佳甚至失去作用。为了同时有效抑制两种干扰信号,提出了一种适用于椒盐-高斯干扰信号的自适应滤波改进算法。该算法首先通过干扰信号噪声点辨识与滤波窗口自适应扩展,计算信号噪声辨识过程中各扩展窗口归一化系数和一次加权联合滤波中间输出,然后利用多层级窗口中间输出值进行二次加权优化滤波,减少干扰信号噪声点对联合滤波输出的影响,最后针对计算量大的问题,在中值滤波过程中提出均值分割方法,提高滤波算法实时性。实验结果表明,该方法能有效抑制椒盐-高斯干扰信号噪声,算法实时性较好,优于多种传统及其演进滤波算法。     

Blind identification of bilinear systems

This paper is concerned with the blind identification of a class of bilinear systems excited by non-Gaussian higher order white noise. The matrix of coefficients of mixed input-output terms of the bilinear system model is assumed to be triangular in this work. Under the additional assumption that the system output is corrupted by Gaussian measurement noise, we derive an exact parameter estimation procedure based on the output cumulants of orders up to four. Results of the simulation experiments presented in the paper demonstrate the validity and usefulness of our approach.     

微弱信号混沌检测方法的抗噪性能研究

针对混沌检测算法中临界状态不易准确判定,相变判别可能受到噪声影响的问题,首先阐述了基于Duffing方程的微弱信号检测原理,然后对混沌检测算法中噪声对系统输出的影响进行了推导和理论分析,设计仿真实验对复杂噪声条件下混沌系统的检测性能进行了分析。理论分析和仿真实验都表明,混沌系统对各种噪声都具有良好的免疫力,可以应用于强噪声背景下微弱信号的检测。     

Estimating second-order Volterra system parameters from noisy measurements based on an LMS variant or an errors-in-variables method

This paper deals with the identification of a nonlinear SISO system modelled by a second-order Volterra series expansion when both the input and the output are disturbed by additive white Gaussian noises. Two methods are proposed. Firstly, we present an unbiased on-line approach based on the LMS. It includes a bias correction scheme which requires the variance of the input additive noise. Secondly, we suggest solving the identification problem as an errors-in-variables issue, by means of the so-called Frisch scheme. Although its computational cost is high, this approach has the advantage of estimating the Volterra kernels and the variances of both the additive noises and the input signal, even if the signal-to-noise ratios at the input and the output are low.     

Blind identification of ARMA channels with periodically modulatedinputs

Previous results have pointed out the importance of inducing cyclostationarity at the transmitter for blind identification of FIR communication channels. The present paper considers the blind identification problem of an ARMA (p,q) channel by exploiting the cyclostationarity induced at the transmitter through periodic encoding of the input. It is shown that causal and stable ARMA (p,q) channels can be uniquely identified from the output second-order cyclic statistics, irrespective of the location of channel poles and zeros and color of additive stationary noise, provided that the cyclostationary input has at least q+1 nonzero cycles     

基于回归算法的证件物品识别器参数辨识模型

证件物品管理识别器参数辨识存在局部最优现象,噪声干扰下辨识精度下降,提出基于回归算法的证件物品管理识别器参数辨识模型.将证件物品管理识别参数输出误差平方和,代入粒子群算法适应度函数,通过粒子群优化算法实时更新粒子个体最优值以及全局最优值,初步辨识证件物品管理识别器参数,并将所获取结果作为支持向量回归算法迭代初始值,利用...