乘性噪声下的弱GPS信号捕获

乘性噪声对弱GPS信号的检测与捕获具有重要影响。在分析乘性噪声的循环平稳特性的基础上,提出一种在乘性噪声条件下的弱GPS信号检测方法。首先通过本地产生的伪随机码与接收到的GPS信号进行位乘,以尝试性地消除伪码对GPS信号二阶统计量的周期性的影响;然后利用去伪码后的GPS信号的循环平稳特性估计GPS信号的载频,从而进一步地完成对GPS信号的检测。同时还提出了一种乘性噪声条件下的弱GPS信号检测结构,分析了基于该检测结构的检测性能。最后通过仿真验证了该方法的有效性。     

一类非线性网络系统中噪声改善信号的相关性

基于相关系数研究了在一类非线性神经网络系统中加性和乘性噪声作用下的阈上随机共振现象。仅在加性噪声或者乘性噪声的作用下,对每一个固定的系统阈值,加性噪声下的阈上随机共振比乘性噪声下的阈上随机共振更容易发生,且相关系数所达到的峰值也比在乘性噪声下的峰值大,这说明加性噪声更有利于改善信号的相关性。系统阈值的增加会降低阈上随机共振的功效;而阈值单元数目的增多,会提高阈上随机共振的功效。加性和乘性噪声共同作用下的阈上随机共振现象同样存在,对系统阈值进行恰当选取和增加系统阈值单元数目使得阈上随机共振现象更加明显;给定乘性噪声而改变加性噪声比固定加性噪声而改变乘性噪声阈上随机共振更容易发生,且功效更好。     

多阈值系统中高斯混合噪声改善信息的传输

讨论高斯混合噪声下多阈值系统中的随机共振现象.对于单峰噪声,当输入信号在阈上时,互信息随着噪声的增强单调递减,噪声总是不利于信息的传输;当信号在阈下时,互信息随着噪声的增强先递增然后再递减,适量的噪声能改善信息传输,随机共振现象存在.对于双峰噪声,信号在阈下或阈上,噪声有时能够改善信息的传输,随机共振和阈上随机共振存在.这些结果说明多阈值系统中噪声改善信息的传输依赖于噪声类型,拓广了随机共振和阈上随机共振在多元信息传输中的应用.     

基于互信息的多阈值系统中随机谐振现象研究

基于互信息讨论了四种典型噪声下多阈值系统中的随机谐振现象.当输入信号在阈上时,互信息随着噪声的增强而单调递减,噪声总是恶化系统的性能;而当输入信号在阈下时,互信息随着噪声的增强先递增然后再递减,适量噪声能改善多阈值系统的性能,随机谐振现象存在.随着阈值系统阈值的增大,随机谐振功效降低,最佳噪声强度逐渐增大.文中结果说明了多阈值系统中基于互信息的随机谐振现象存在.且随机谐振对噪声具有一定的鲁棒性,拓广了随机谐振在多元信号与信息处理中的应用.     

含不同时滞项的双稳系统中平均首次穿越时间的研究

研究了乘性噪声和加性噪声共同作用下含有两种不同时滞项的双稳系统中的平均首次穿越时间．首先通过近似方法得到了平均首次穿越时间的解析式，然后研究了乘性噪声强度、时滞量及噪声关联强度对平均首次穿越时间的影响．当噪声关联强度取正值时，平均首次穿越时间T1（x-→x＋）是乘性噪声强度及两种时滞量的非但调函数，是噪声关联强度的单调递增函数．包含在确定力与振荡力中的时滞量分别影响T1（x-→x＋）的最大值及对应的噪声强度．平均首次穿越时间T2（x＋→x-）是包含在确定力中的时滞量的非单调函数，是乘性噪声强度、另一种时滞量及噪声关联强度的单调递减函数．     

相关乘性和加性高斯白噪声激励下周期势系统的随机共振

研究了外加周期信号作用下,相关高斯乘性和加性白噪声激励下周期势系统的随机共振.利用线性响应理论,计算了系统输出信号的功率谱密度、振幅、相位差.研究结果表明:当加性噪声强度和关联系数不变的情况下,通过调整乘性噪声强度可以出现随机共振;关联系数的正负以及大小对随机共振的影响较小.当乘性噪声强度较小时,输出信号的振幅和相位差曲线有一个单峰出现,即出现随机共振现象,能量从噪声向信号进行转化.随着噪声强度的增大,随机共振现象消失,噪声由增大系统的有序程度渐渐变为增大系统的无序程度.     

传感通信网络过滤方法研究与仿真

传感通信网络中乘性噪声的过滤,可提高通信网络的性能。在传感通信网络乘性噪声过滤过程中,由于传感网络的信道大部分处于移动状态中,信道路线的变化会造成不同节点间的乘性噪声干扰,传统的传感网络方法过滤乘性噪声,信号与噪声之前缺少识别过程,统一转换成固定特征噪声加以删除,过滤效果较差。提出采用改进主分量分析算法的传感通信网络乘性噪声的过滤方法。对采集到的初始信号进行白化与降维处理,使初始有色信号转换成白色信号,且分量彼此间相互独立,减少处理过程的数据量,根据改进主分量分析算法构造信号数据矩阵,进行正交变换获取有效特征值并进行信号重构,实现对传感通信网络中乘性噪声的有效过滤。实验结果表明,利用改进算法进行传感通信网络乘性噪声的过滤,能够提高噪声过滤的精度,提高传感通信网络性能,具有极大的优越性。     

基于离散双向联想记忆神经网络的多元通信系统

针对噪声导致非线性数字通信系统传输信号的差错概率增加的问题，提出一种基于离散双向联想记忆（BAM）神经网络的多元通信系统。首先，根据需要传输的信号，选取适当的神经元数量和记忆向量，计算权值矩阵，并生成BAM神经网络；然后将多元信号映射为具有调制幅度的初始输入向量并不断输入系统，通过神经网络进行循环迭代，并向各神经元添加高斯噪声，之后按照码元间隔采样输出并在无损信道中传输，接收端依据判决规则译码判决；最后在图像处理领域，利用所提系统传输图像压缩后的数据并解码恢复图像。仿真结果表明，对于码元间隔较大的弱调制信号，随着噪声强度的增加，差错概率先减后增，随机共振现象比较明显；差错概率还与信号的进制数呈正相关关系，与信号幅度、码元间隔和神经元个数呈负相关关系，某些条件下，差错概率可以达到0。以上结果表明BAM神经网络可以通过噪声改善数字通信系统的可靠性。另外，解码恢复图像的相似度显示了适量噪声对图像恢复效果的改善，扩展了BAM神经网络和随机共振在图像压缩编码中的应用。     

基于总体最小二乘法的图像降噪

来自图像传感器的数字图像会受到各种噪声的干扰,其中主要包括加性噪声、乘性噪声和混合噪声。乘性噪声随信号幅度改变而改变,没有理想的去除方法。为此,运用基于总体最小二乘法的图像估计降噪方法,研究图像块尺寸选取对降噪性能的影响,分析成像系统中去马赛克环节影响噪声传播的内在规律,并通过比较实验给出总体最小二乘法降噪的性能优势。     

适用于Chirp信号的语音消噪改进算法

采用Chirp信号为载波与各种数字调制方式相结合的Chirp调制信号作为一种数传工具,具有很强的可靠性和鲁棒性。然而接收机上的Chirp接收信号常常伴有周边广播电台发射出的语音噪声干扰。随着广播电台的发展,语音噪声的能量有逐渐变强的趋势,严重影响Chirp信号传输的可靠性和有效性。由于语音噪声的非平稳性,传统的消噪方法难以取得较好的效果。提出一种适用于Chirp信号的语音消噪算法,对短波接收机上的Chirp-BPSK信号进行消噪处理。接收信号上的加性语音噪声被大幅度削减,系统的误码率性能得到很大提升,验证了该消噪算法的有效性。     

非加性噪声情形下卡尔曼滤波器的推广

在数字信号处理中,得到的信号总是或多或少伴随着噪声。如何去除噪声,恢复真实的信号,是信号处理面临的首要问题。一般情形下我们都假定噪声是加性的,即噪声是不依赖于信号的,此时,卡尔曼滤波器是一种非常简便的降噪方法,它是一个最优化自回归数据处理算法,是用前一个估计值和最近一个观察数据来估计信号的当前值,是用状态方程和递推的方法进行估计的,而且它在均方误差意义下是最优的。本文将噪声推广到一般的乘性噪声的情形,利用卡尔曼滤波的基本思想,同样可以得到均方误差意义下的最优滤波,最后通过一个模拟的例子验证了该方法的有效性。     

Improving the state estimation for optimal control of stochastic processes subject to multiplicative noise

Computational models for the neural control of movement must take into account the properties of sensorimotor systems, including the signal-dependent intensity of the noise and the transmission delay affecting the signal conduction. For this purpose, this paper presents an algorithm for model-based control and estimation of a class of linear stochastic systems subject to multiplicative noise affecting the control and feedback signals. The state estimator based on Kalman filtering is allowed to take into account the current feedback to compute the current state estimate. The optimal feedback control process is adapted accordingly. The resulting estimation error is smaller than the estimation error obtained when the current state must be predicted based on the last feedback signal, which reduces variability of the simulated trajectories. In particular, the performance of the present algorithm is good in a range of feedback delay that is compatible with the delay induced by the neural transmission of the sensory inflow.     

Optimal filtering for polynomial system states with polynomial multiplicative noise

In this paper, the optimal filtering problem for polynomial system states with polynomial multiplicative noise over linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. The procedure for obtaining a closed system of the filtering equations for any polynomial state with polynomial multiplicative noise over linear observations is then established, which yields the explicit closed form of the filtering equations in the particular cases of a linear state equation with linear multiplicative noise and a bilinear state equation with bilinear multiplicative noise. In the example, performance of the designed optimal filter is verified for a quadratic state with a quadratic multiplicative noise over linear observations against the optimal filter for a quadratic state with a state‐independent noise and a conventional extended Kalman–Bucy filter. Copyright © 2006 John Wiley & Sons, Ltd.     

Preview control for a class of linear stochastic systems with multiplicative noise

ABSTRACT

In this paper, the preview control problem for a class of linear continuous time stochastic systems with multiplicative noise is studied based on the augmented error system method. First, a deterministic assistant system is introduced, and the original system is translated to the assistant system. Then, the integrator is employed to ensure the output of the closed-loop system tracking the reference signal accurately. Second, the augmented error system, which includes integrator vector, control vector and reference signal, is constructed based on the system after translation. As a result, the tracking problem is transformed into the optimal control problem of the augmented error system, and the optimal control input is obtained by the dynamic programming method. This control input is regarded as the preview controller of the original system. For a linear stochastic system with multiplicative noise, the difficulty being unable to construct an augmented error system by the derivation method is solved in this paper. And, the existence and uniqueness solution of the Riccati equation corresponding to the stochastic augmented error system is discussed. The numerical simulations show that the preview controller designed in this paper is very effective.     

Edge detection in bar code signals corrupted by integrated time-varying speckle

Accurate edge localization is of primary importance in bar code decoding. In this paper we investigate statistical properties of edge localization error when a bar code signal is corrupted by additive noise which is a weakly stationary random process. We derive a first order approximation for the expected value and standard deviation of the error in terms of the power spectral density (PSD) of the noise. This result is used to estimate the edge localization error caused by speckle noise. We show that the standard deviation of the error is determined by the intensity distribution of a scanning beam and the positions of neighboring edges. We discuss how the error analysis determines the detection rates of a scanning system, and how it can be used in the system design. Analytical examples are provided for UPCA bar code symbols and a scanning beam with Gaussian distribution.     

Document cleanup using page frame detection

When a page of a book is scanned or photocopied, textual noise (extraneous symbols from the neighboring page) and/or non-textual noise (black borders, speckles, ...) appear along the border of the document. Existing document analysis methods can handle non-textual noise reasonably well, whereas textual noise still presents a major issue for document analysis systems. Textual noise may result in undesired text in optical character recognition (OCR) output that needs to be removed afterwards. Existing document cleanup methods try to explicitly detect and remove marginal noise. This paper presents a new perspective for document image cleanup by detecting the page frame of the document. The goal of page frame detection is to find the actual page contents area, ignoring marginal noise along the page border. We use a geometric matching algorithm to find the optimal page frame of structured documents (journal articles, books, magazines) by exploiting their text alignment property. We evaluate the algorithm on the UW-III database. The results show that the error rates are below 4% each of the performance measures used. Further tests were run on a dataset of magazine pages and on a set of camera captured document images. To demonstrate the benefits of using page frame detection in practical applications, we choose OCR and layout-based document image retrieval as sample applications. Experiments using a commercial OCR system show that by removing characters outside the computed page frame, the OCR error rate is reduced from 4.3 to 1.7% on the UW-III dataset. The use of page frame detection in layout-based document image retrieval application decreases the retrieval error rates by 30%.     

图像处理中维纳滤波器的推广与应用

维纳滤波器是一种最简便的降噪方法,而且它在均方误差意义下是最优的。将噪声推广到一般的乘性噪声的情形,利用维纳滤波的基本思想,同样可以得到均方误差意义下的最优滤波,最后通过两个模拟的例子验证了该方法的有效性。     

Stochastic resonance in parallel concatenated turbo code decoding

In an array of threshold devices, we examine the effect of noise in improving performance of turbo code decoding. Such a phenomenon of noise-enhanced effect is termed stochastic resonance (SR). When signal is subthreshold, SR is observed by using Gaussian noise during iterative decoding. That is, the minimal bit error ratio (BER) is achieved at some non-zero noise intensity level. Besides, the larger the number of threshold devices is, the more remarkable the SR effect becomes. Especially when noise intensity is nearly optimal, BER approximates to zero after a few decoding iterations. Moreover, when Gaussian mixture noise is utilized, suprathreshold stochastic resonance (SSR) occurs in turbo decoding. These results show the beneficial effect of noise in channel coding and decoding.