脉冲噪声中基于指数函数的可变拖尾非线性变换设计

针对脉冲噪声中的信号检测问题,该文提出一种基于指数函数的非线性变换函数设计与优化方法。该方法利用指数函数衰减速度可调的优点,适用于脉冲噪声的各种分布模型。通过引入效能函数,将非线性函数设计问题转化为以效能最大化为目标的阈值与底数参数优化问题。由于效能是关于待优化参数的连续可导且单峰函数,该优化问题可采用数值优化方法如单纯形法快速稳健地求解。性能分析表明,针对脉冲噪声常用的对称α稳定分布、Class A分布和高斯混合分布,该文方法均能取得基本最优检测性能,基于实测大气噪声仿真的通信误码率也明显优于传统的削波器和置零器。因此,该文为各种分布的脉冲噪声提供了一个统一的最优抑制解决方法。     

抑制脉冲型噪声的限幅器自适应设计

针对脉冲型噪声的抑制问题,该文提出一种自适应的限幅器设计方法。该方法以效能函数为指标,采用自适应搜索算法,自动寻找削波器和置零器的最佳门限,且能适用于未知噪声分布的情形。首先分析了效能与非线性函数的关系,给出关键的优化问题。然后考虑到效能函数计算复杂,提出基于线搜索的自适应设计算法。其次针对未知分布情况,考虑非参数化的概率密度估计,该算法能够稳健运行且基本取得最优设计效果。最后,结合两种非高斯噪声和实测大气噪声数据仿真,结果表明:该文方法可自适应寻找最佳门限,使削波器和置零器效能达到最佳;当噪声分布未知时,该文方法无需假设噪声模型,可与非参数化概率密度估计方法结合,取得最优检测效果。     

对称α稳定分布噪声环境下短波衰落信号时延估计的新算法

脉冲噪声环境下信号时延估计是信号处理领域十分活跃的研究方向。在对称α稳定分布噪声环境下,文章提出了一种基于非线性压缩核函数（NCCF）的短波衰落信号的时延估计算法。该算法采用非线性压缩核函数抑制了对称α稳定分布噪声的脉冲拖尾,同时较好保持了目标信号之间的互相关特性,从而保证了信号时延估计的准确性。同以往算法相比,本算法不仅降低了计算复杂度,而且对于衰落信号的时延估计具有更强的稳定性。理论证明和仿真结果均表明,该算法比GCC、FLOC、GCF和非线性变换等算法具有更好的时延估计有效性和准确度。      

α稳定分布噪声中谐波恢复的非线性变换方法

本文研究α稳定分布噪声中谐波恢复的问题.文中首先引入α稳定分布作为模拟脉冲性噪声的模型,然后采用非线性变换方法使噪声得到抑制,并证明了变换后的含噪观测值具有二阶统计量.对变换后的矢量空间进行预白化处理后结合噪声子空间的MUSIC方法恢复谐波信号参数.本文使用了两种非线性变换方法,分析了它们的变换性能、讨论了变换参数对谐波恢复效果的影响以及两种非线性变换间的关系,分别给出了仿真结果.最后得出非线性变换Sigmoid函数在α稳定分布噪声中恢复谐波信号具有较强的韧性.     

基于直方图的自适应图像去噪滤波器

对于那些明显偏离高斯型白噪声的加性噪声,如拖尾脉冲噪声,高斯脉冲噪声等,已有方法的滤噪性能会严重退化.为此,该文提出了一种去除脉冲噪声的新方法.该方法首先由被污染图像估计出原图像的直方图.然后应用模糊集理论,利用加权策略得到了一个符合图像灰度分布统计规律的模糊隶属度函数,以此隶属度函数构建一个加权平均滤波器. 新方法有效地利用了原图像的先验知识,能够根据图像区域特性差异及脉冲噪声强弱自适应地采用不同的滤波尺度.文章比较了传统滤波器、已有的模糊滤波器和本文方法的结果.实验表明本文方法具有更好的效果.     

SαS分布噪声环境下高分辨率二维DOA估计算法及快速实现

本文设计出一种针对脉冲噪声的二维鲁棒高分辨率波达方向（DOA,Direction of Arrival）估计算法,以解决雷达、声纳等无线通信领域中脉冲噪声环境下IAA（Iterative Adaptive Approach）无法准确估计出DOA的问题.该算法中,用最小p阶范数代替WLS（Weighted Least Squares）作为最优化求解的代价函数.此外,根据Toeplitz-Block-Toeplitz（TBT）矩阵性质和FFT简化计算过程,提出该算法的快速实现方法,提高算法的计算效率.该算法在对称α-稳定（SαS,Symmetric Alpha-Stable）分布噪声环境下建模,仿真结果表明：与CRCO-MUSIC（CoRrentropy based COrrelation-MUltiple Signal Classification）算法和MUSIC-FLOM（MUltiple Signal Classification-Fractional Lower-Order Moment）算法相比,二维lp-IAA算法可以在低信噪比、单快拍条件下有效分辨出相邻多目标信号;快速算法可以在保证高分辨率的前提下,算法平均运算时间降低至原来的约1/40.     

冲击噪声背景下基于归一化的线性约束特征干扰相消器

针对冲击噪声背景下,常规波束形成算法性能下降的问题,本文提出一种适用于任意未知统计特性的代数拖尾冲击噪声环境下的基于归一化的线性约束特征干扰相消器（N-LCEC）算法。该算法在附加线性约束的条件下,以噪声功率最小化为目标函数;通过对输入信号进行无穷范数归一化,使变换信号的二阶统计量在代数拖尾的冲击噪声环境下存在且有界,然后将自适应权矢量约束于噪声子空间的方法,提高了波束形成器在冲击噪声背景下的性能。N-LCEC算法无需噪声特征指数的先验信息,适用冲击噪声环境更广;N-LCEC算法具有运算简单,干扰抑制能力强,同时保持静态方向图的副瓣特征等优点。仿真结果验证了该算法的有效性和优越性。      

一种改进的电力线通信系统脉冲噪声抑制方法

为了抑制脉冲噪声对电力线正交频分复用（OFDM）通信系统的影响,最常用的方法之一是在接收端OFDM解调器之前前置一个置零非线性单元,即传统置零法。然而,由于引入了非线性失真,其性能并不理想。针对传统置零法引起的非线性失真问题,提出了一种基于迭代消除非线性失真的改进置零法。首先,对接收到的时域OFDM信号进行脉冲噪声检测和置零处理;然后,在频域利用已检测的符号来重构时域置零处理引入的非线性失真,并通过迭代提高重构的准确性;最后,从频域接收信号中减去重构的非线性失真。仿真结果表明,所提改进算法与传统置零法相比,有非常大的性能提升,增强了电力线OFDM通信系统对脉冲噪声的抵抗能力。     

非线性系统中状态和参数联合估计的双重粒子滤波方法

该文提出了一种双重粒子滤波方法,对存在未知参数的非线性系统进行状态和参数联合估计。该方法采用基于充分统计量的粒子滤波技术,避免了重采样过程中的粒子枯竭现象;采用贝塔分布拟合系统参数的后验分布,不仅充分利用了先验信息,而且避免了对高斯分布拖尾部分的采样,提高了粒子的采样效率。仿真实验结果表明,该方法提高了非线性系统中状态和参数的估计精度,降低了滤波器对初始误差的敏感性。     

三阶色散对光纤中基孤子脉冲传输的影响

通过数值求解非线性薛定谔方程,研究了三阶色散效应对基孤子脉冲在单模光纤中传输的影响.结果表明,在三阶色散作用下,基孤子脉冲在传输过程中被展宽,峰值能量逐渐减小,脉冲形状发生畸变,脉冲的中心偏向一侧,并形成非对称的拖尾振荡结构;当与二阶色散共同作用时,三阶色散引起的脉冲振荡会变缓.考虑非线性效应后,在一定程度上补偿了由于色散效应导致的峰值功率的减小,同时抑制了非对称拖尾结构的产生.     

Maximum likelihood localization of sources in noise modeled as astable process

This paper introduces a new class of robust beamformers which perform optimally over a wide range of non-Gaussian additive noise environments. The maximum likelihood approach is used to estimate the bearing of multiple sources from a set of snapshots when the additive interference is impulsive in nature. The analysis is based on the assumption that the additive noise can be modeled as a complex symmetric α-stable (SαS) process. Transform-based approximations of the likelihood estimation are used for the general SαS class of distributions while the exact probability density function is used for the Cauchy case. It is shown that the Cauchy beamformer greatly outperforms the Gaussian beamformer in a wide variety of non-Gaussian noise environments, and performs comparably to the Gaussian beamformer when the additive noise is Gaussian. The Cramer-Rao bound for the estimation error variance is derived for the Cauchy case, and the robustness of the SαS beamformers in a wide range of impulsive interference environments is demonstrated via simulation experiments     

Parameter estimation and blind channel identification in impulsivesignal environments

New methods for parameter estimation and blind channel identification in impulsive signal environments are presented, where the signals/noise are modeled as symmetric α-stable (SαS) processes. First, we present methods for estimating the parameters (characteristic exponent α and dispersion γ) of a SαS distribution from a time series. The fractional lower order moments, with both positive and negative orders, and their applications to signal processing are introduced. Then we present a new algorithm for blind channel identification using the output fractional lower order moments, and the α-Spectrum, a new spectral representation for impulsive signals, is introduced. From the α-Spectrum, we establish the blind identifiability conditions of any FIR channel (mixed-phase, unknown order) with i.i.d. SαS (α>1) input. As a byproduct, a simple algorithm for recovering the phase of any type of a signal from the magnitude of its z-transform is presented. The novelty of our paper is in parameter estimation and blind identification of the FIR channel based on fractional lower order moments of its output data. Monte Carlo simulations clearly demonstrate the performance of the new methods     

A subspace-based direction finding algorithm using fractional lowerorder statistics

We propose several classes of fractional lower order moment (FLOM)-based matrices that can be used with MUSIC to estimate the DOAs of independent circular signals embedded in additive SαS (symmetric α stable) noise (e.g., sea clutter). We run simulations with different choices of the FLOM parameter p for our FLOM-based matrices and conclude that when the noise is SαS with unknown α≠2, FLOM-multiple signal classification (MUSIC) with p close to unity yields good performance. The performance of FLOM-MUSIC and robust covariation-based (ROC)-MUSIC are similar. Three scenarios that contain circular signals (phase modulation (PM), circularly symmetrical Gaussian, and quaternary phase-shift keying (QPSK)) and one scenario that contains noncircular signals (binary phase-shift keying (BPSK)), all embedded in the same SαS noise, are tested. These simulation results reveal that the scenario containing BPSK signals leads to poor performance, indicating that FLOM-MUSIC is presently limited to circular signals     

冲击噪声环境下基于特征函数的调制识别算法

无线通信环境中不仅存在高斯噪声,而且存在冲击噪声。通过分析复循环对称稳定冲击噪声的特点,该文提出利用接收信号特征函数相位进行调制识别的方法。分类特征向量由不同角度对应的特征函数相位组成,对未知信号采用最小欧式距离法进行分类。理论分析证明,采用特征函数相位作为分类特征,可以完全抑制各种特征指数的分布噪声。仿真结果表明,在冲击噪声及高斯噪声环境下,信噪比大于8dB时,平均识别正确率都高于90％,该算法分类效果良好。     

Symmetric alpha-stable filter theory

Symmetric α-stable (SαS) processes are used to model infinite-variance impulsive noise. In general, Wiener filter theory is not meaningful in (SαS) environments because the expectations may be unbounded. To develop a theory for linear finite impulse response systems with independent identically distributed (SαS) inputs, we propose median orthogonality as a linear filter criterion, derive a generalized Wiener-Hopf solution equation, and show a sufficient condition for a filter to achieve the criterion. For non-Gaussian (SαS) densities, zero-forcing least-mean-squares is the only well-known filter that satisfies the criterion, but others can be designed. We present a second algorithm and simulations showing that both converge to the generalized Wiener-Hopf solution     

On the existence of universal nonlinearities for blind sourceseparation

Many density-based methods for blind signal separation employ one or more models for the unknown source distribution(s). This paper considers the issue of density model mismatch in maximum likelihood (ML)-type blind signal separation algorithms. We show that the score function nonlinearity, which was previously derived from the standpoint of statistical efficiency, is also the most robust in maintaining a separation solution for the ML algorithm class. We also consider the existence of a universally applicable nonlinearity for separating all signal types, deriving two results. First, among nonlinearities with a convergent Taylor series, a single fixed nonlinearity for universal separation using the natural gradient algorithm cannot exist. Second, among nonlinearities with a single adjustable parameter, a previously proposed threshold nonlinearity can separate all signals with symmetric amplitude distributions as long as the threshold parameter is properly chosen. The design of "difficult-to-separate" signal distributions is also discussed     

Adaptive robust spread-spectrum receivers

We consider the problem of robust detection of a spread-spectrum (SS) signal in the presence of unknown correlated SS interference and additive non-Gaussian noise. The proposed general SS receiver structure is comprised by a vector of adaptive chip-based nonlinearities followed by an adaptive linear tap-weight filter and combines the relative merits of both nonlinear and linear signal processing. The novel characteristics of our approach are as follows. First, the nonlinear receiver front-end adapts itself to the unknown prevailing noise environment providing robust performance for a wide range of underlying noise distributions. Second, the adaptive linear tap-weight filter that follows the nonlinearly processed chip samples results in a receiver that is proven to be effective in combating SS interference as well. To determine the receiver parameters, we propose, develop, and study three adaptive schemes under a joint mean-square error (MSE), or a joint bit-error-rate (BER), or a joint MSE-BER optimization criterion. As a side result, we derive the optimum decision fusion filter for receivers that utilize hard-limiting (sign) chip nonlinearities. Numerical and simulation results demonstrate the performance of the proposed schemes and offer comparisons with the conventional matched-filter (MF), the decorrelator, the conventional minimum-variance-distortionless-response (MVDR) filter, and the sign-majority vote receiver     

基于新的遗传算法的模糊C均值聚类用于遥感图像分割

标准FCM对噪声十分敏感,并且依赖于初始聚类中心选择,算法通常得到的是局部最优解而非全局最优解。针对此问题提出一种基于猴王遗传算法的改进的FCM算法.猴王遗传算法是一种新颖的全局优化搜索算法,具有高效的计算性能和优良的全局搜索能力。本文首次将猴王遗传算法（MKGA）与结合空间领域信息的FCM相结合,利用改进的FCM算法的目标函数建立适应度函数,利用猴王遗传算法搜索全局最优解,代替FCM的基于梯度下降的迭代过程,从而有效地避免了模糊C-均值聚类算法收敛到局部最优和对噪声敏感的问题。在此基础上实现了对遥感图像的聚类分割。实验结果表明,该算法对于遥感图像显示了较好的分割效果和较强的抗噪能力。