小波变换和AR模型在脑电信号处理中的应用

谈论了基于小波变换和AR模型的(EEG)信号的分析方法,在两种方法相结合的情况下,能有效消去(EEG)信号的噪声。用小波变换对含有瞬态干扰的脑电信号进行多尺度分解,在某些尺度下,瞬态信号特征得以明显增强,用简单的阈值比较就可以有效地检测并消除瞬态干扰。最后在Matlab环境下进行仿真实验,验证了此方法在提取脑电信号中的有效性。     

瞬态信号双谱估计的一种新方法

瞬态信号的模型参数估计在许多领域占据要当重要地位，本文利用高阶矩的性质，提出一种Ｑ－ｓｌｉｃｅ马尔可夫ＡＲＭＡ模型双谱估计算法，仿真结果表明，该方法估计精度高，计算量小，即使在低信噪比的悟上，对瞬态信号了有较好的拟合效果。     

串联MEMS开关的瞬态电磁场分析

提出了一种串联MEMS开关的电磁耦合模型 ,并且应用该模型 ,对采用表面硅工艺和体硅工艺制作的MEMS开关 ,采用全波分析方法 ,进行了瞬态电磁场分析。由于开关尺寸为微米量级 ,而驱动电压高达 4 0～ 6 0V ,这样的瞬态高压有可能对开关上的信号产生影响。理论仿真结果显示 ,开关驱动路对信号路有很强的耦合场存在。实验结果同样显示 ,耦合到信号路的信号可以输入信号产生最大值为 6 0 %的失真     

串联MEMS开关的瞬态电磁场分析

提出了一种串联MEMS开关的电磁耦合模型,并且应用该模型,对采用表面硅工艺和体硅工艺制作的MEMS开关,采用全波分析方法,进行了瞬态电磁场分析.由于开关尺寸为微米量级,而驱动电压高达40～60V,这样的瞬态高压有可能对开关上的信号产生影响.理论仿真结果显示,开关驱动路对信号路有很强的耦合场存在.实验结果同样显示,耦合到信号路的信号可以输入信号产生最大值为60%的失真.     

串联MEMS开关的瞬态电磁场分析

提出了一种串联MEMS开关的电磁耦合模型，并且应用该模型，对采用表面硅工艺和体硅工艺制作的MEMS开关，采用全波分析方法，进行了瞬态电磁场分析。由于开关尺寸为微米量级，而驱动电压高达40～60V，这样的瞬态高压有可能对开关上的信号产生影响。理论仿真结果显示，开关驱动路对信号路有很强的耦合场存在。实验结果同样显示，耦舍到信号路的信号可以输入信号产生最大值为60％的失真。     

噪声对消在信号处理系统中的应用

论述基于LMS算法的自适应滤波器噪声对消的工作原理,以及基于AR模型的信号分析方法。在这两种方法相结合的情况下,能有效去除信号的噪声。对含有瞬态干扰的微弱信号,用AR模型法估计出噪声的系数并预测噪声波形,通过自适应滤波器噪声对消原理进行滤波,最后在Matlab环境下进行仿真试验,结果表明该方法具有较好的去噪效果。     

基于偏振分析的瞬态干扰抑制方法

瞬态干扰的存在会严重影响矢量信号处理方法性能,必须对其抑制。该文提出一种基于偏振分析的瞬态干扰抑制方法。该方法提取矢量信号的偏振参数,构建极化滤波器,对接收信号滤波,去除瞬态干扰。通过极化滤波器的调制函数判断瞬态干扰的存在位置,再利用信号的偏振模型恢复瞬态干扰处信号。仿真结果和实验数据表明该方法可以有效地抑制瞬态干扰。     

零电流开关准谐振变换器的建模分析

论述了零电流准谐振变换器的工作原理,给出了建立谐振变换器的非线形模型,并用MATLAB进行了仿真.结果表明,利用此种模型可以得到精确的大信号瞬态响应.     

一维不均匀介质中波传播瞬态响应的传输线模型分析

提出了用传输线模型分析一维不均匀介质中波传播的瞬态响应。这种方法将一维不均匀介质用多层介质近似，并进一步用级联的传输线模型等效。分析的过程是：首先对局限在某一时间段内的输入电磁信号进行傅里叶变换得出其频谱，然后研究该频谱的主要分量在所研究介质中的传播，并得出波传播方向任一截面上瞬态信号的频谱，最后通过快速傅里叶变换得出时域瞬态响应。这种方法适用范围广，数值计算方便，只要离散时间间隔足够小，抽样点数足够大，就能达到一定的精度。     

一个简单的量子阱激光器等效电路模型

给出一个新的量子阱激光器等效电路模型,由量子阱激光器单模速率方程推导得到并在电路模拟程序ＳＰＩＣＥ中完成。该模型考虑了热辐射效应和分离限制区域（ＳＣＨ）内的载流子工作情况,给出了新的光增益表达式。并利用该模型对单量子阱激光器的小信号特性和瞬态大信号特性进行了预测,模拟结果表明和速率方程的直接求解结果吻合很好。     

A NEW METHOD OF BISPECTRAL ESTIMATION FOR TRANSIENT SIGNAL

Estimation of model parameter for transient signal is very important in many aspects. This paper presents a new Markov ARMA model Q-slice estimation algorithm for transient signal based on bispectrum. Simulation results show that this new method has some special features, such as higher estimation precision, lower amount of calculation, higher fitting effect even in lower signal-to-noise ratio (SNR) situation.     

Bispectrum Reconstruction Method of UWB Echoes

Estimation of transient signal in additive noise is very important in radar object detection and recognition. This paper presents a new method for transient signal reconstruction based on bispectrum estimation techniques. The third-order cumulants of the received noisy ultra-wide band echoes are acquired first and an ARMA model is then fitted. The bispectrum of the output signal of the ARMA model will be used to reconstruct the transient signal. Simulation results show that the effect is very good even in lower signal-to-noise (SNR) situation.     

指数阻尼正弦模型阶选择算法

 本文针对指数阻尼正弦信号模型提出了一种新的基于子空间和结构总体最小范数的阶选择算法.该方法利用信号子空间移不变性以及阶与信号分量数匹配时总体误差最小原理建立模型阶估计准则.实验表明该方法获得了更高的阶估计精度.算法无需设置阈值和惩罚项,自动完成阶估计过程.它可以应用到高分辨率的谐波恢复算法中.     

基于二分搜索的宽带多普勒模型信号捕获方法

针对深空通信等超高动态低信噪比通信场景中,传统信号捕获方法存在着动态范围不足、精确度不够以及使用窄带多普勒模型导致对接收信号近似精确度不高的问题,提出一种基于二分搜索的接收端采样率调整方法。使用宽带多普勒模型对接收信号进行建模,并使用扫频余弦类信号进行多普勒频偏估计和定时估计,处理多普勒伸缩后的信号与本地信号采样率不匹配的问题。对所提方法进行同步性能仿真,仿真结果表明,所提方法采用正反扫频线性调频(UD-LFM)信号,能在信噪比为-49 dB、最大多普勒频偏为2 MHz的条件下,多普勒频偏估计误差小于400 Hz,定时估计误差不超过60 ns。相比不使用本文所提方法进行直接捕获,本文所提方法具有更高的捕获精确度以及更低的信噪比门限。     

Analysis of mean-square error and transient speed of the LMSadaptive algorithm

For the least mean square (LMS) algorithm, we analyze the correlation matrix of the filter coefficient estimation error and the signal estimation error in the transient phase as well as in steady state. We establish the convergence of the second-order statistics as the number of iterations increases, and we derive the exact asymptotic expressions for the mean square errors. In particular, the result for the excess signal estimation error gives conditions under which the LMS algorithm outperforms the Wiener filter with the same number of taps. We also analyze a new measure of transient speed. We do not assume a linear regression model: the desired signal and the data process are allowed to be nonlinearly related. The data is assumed to be an instantaneous transformation of a stationary Markov process satisfying certain ergodic conditions     

Parameter estimation based on fractional power spectrum under alpha-stable distribution noise environment in wideband bistatic MIMO radar system

This paper takes the alpha-stable distribution as the noise model and works on the parameter estimation problem of wideband bistatic Multiple-Input Multiple-Output (MIMO) radar system in the impulsive noise environment. In many applications, it is not appropriate to approximate the wideband signal by the narrowband model. Furthermore, the echo signal may be corrupted by the non-Gaussian noise. The conventional algorithms degenerate severely in the impulsive noise environment. Thus, this paper proposes a new wideband signal model and a novel method in wideband bistatic MIMO radar system. It combines the fractional lower order statistics and fractional power spectrum, for suppressing the impulse noise and estimating parameters of the target. Firstly, a new signal array model is proposed under the alpha-stable distribution noise model. Secondly, Doppler stretch and time delay are jointly estimated by peak searching of the FLOS-FPSD. Furthermore, two modified algorithms are proposed for the estimation of the direction-of-departure and direction-of-arrival, including the fractional power spectrum density based on MUSIC algorithm (FLOS-FPSD-MUSIC) and the fractional lower-order ambiguity function based on ESPRIT algorithm (FLOS-FPSD-ESPRIT). Simulation results are presented to verity the effectiveness of the proposed method.     

Off-grid DOA estimation algorithm based on unitary transform and sparse Bayesian learning

A rapid off-grid DOA estimating method of RV-OGSBL was raised based on unitary transformation,against the problem of traditional sparse Bayesian learning (SBL) algorithm in solving effectiveness of signal’s DOA estimation under condition of lower signal noise ratio (SNR).Actual received signal of uniform linear array was generated through constructing augment matrix as the processing signal used by DOA estimation.Then,estimation model was transformed from complex value to real value by using unitary transformation.In the next step,off-grid model and sparse Bayesian learning algorithm were combined together to process the realization of DOA estimation iteratively.The accuracy of estimation could made relatively high.The simulation result demonstrates that the RV-OGSBL method not only maintains the performance of traditional SBL algorithm,but also reduces the computational complexity significantly.Under the situation of lower signal noise ratio (SNR) and low number of snapshots,the running time of algorithm is reduced about 50%.This shows the RV-OGSBL method is a rapid DOA estimation algorithm.     

Adaptive estimation of latency changes in evoked potentials

Changes in latency of evoked potentials (EP) may indicate clinically and diagnostically important changes in the status of the nervous system. A low signal-to-noise ratio of the EP signal makes it difficult to estimate small, transient, time-varying changes in latency, or delays. Here, the authors present an adaptive algorithm that estimates small delay (latency change) values even when EP signal amplitudes are time-varying. When the delay is time invariant, the adaptive algorithm produces an unbiased estimate with delay estimation error less than half of the sampling interval. A lower estimation error variance is obtained when, in a pair of signals, the adaptive algorithm delays the signal with the higher SNR. The adaptive delay estimation algorithm was tested on intra-operative recordings of somatosensory EP, and analysis of those recordings reveals that the anesthetic etomidate produces a step change in the amplitude and latency of the EP signals     

新的盲信道估计算法

研究了各种盲信道估计算法.提出一种关于信道的假设,并在此基础上提出一种新的盲信道估计算法.新算法能够同时估计出信号和信道.新算法基于MMSE准则,利用m进制通信系统中,在接收端和发射端都已知信号模式且模式个数有限(K=log2m)的特点,结合预置于算法的信道模型,得到信道模型和信号模式的估计.然后依据MMSE准则得到信道和信号的最佳估计.最后给出仿真结果,仿真表明:新的盲信道估计方法能够精确估计实际信道的等效冲击响应,并能够进行有效通信.     

高阶累积量在谱估计中的应用

本文提出了两种利用高阶累积量估计MA参数的新算法。当信号可以看作一个非高斯白噪声通过一个线性时不变系统的输出时,新算法运算简单、估计准确。本文还通过模拟实验分析了两种算法的性能。