Cumulant-based inverse filters for blind deconvolution

Blind deconvolution of linear time-invariant (LTI) systems has received wide attention in various fields such as data communication and image processing. Blind deconvolution is concerned with the estimation of a desired input signal from a given set of measurements. This paper presents a technique for reconstructing the desired input from only the available corrupted data. The estimator is given in terms of an autoregressive moving average (ARMA) innovation model. This technique is based on higher order statistics (HOS) of a non-Gaussian output sequence in the presence of additive Gaussian or non-Gaussian noise. The algorithm solves a set of overdetermined linear equations using third-order cumulants of the given non-Gaussian measurements in the presence of additive Gaussian or non-Gaussian noise. The inverse filter is a finite impulse response (FIR) filter. Simulation results are provided to show the effectiveness of this method and compare it with a recently developed algorithm based on maximizing the magnitude of the kurtosis of estimate of the input excitation.     

The estimation of the order of an ARMA process using third-order statistics

The paper proposes a new approach to find an autoregressive moving average (ARMA) model order. The basic idea is to extend the previous approach proposed by Liang et al. to third order statistics (TOS). The algorithm uses data matrices rather than calculating cumulants of the observed signal. Hence, we avoid the non-stationary effects, which is due to finite-length observations. The system is driven by a zero-mean independent and identically distributed (i.i.d.) non-Gaussian process. The input signal is unobservable. The observed sequence is corrupted by a zero-mean additive Gaussian noise. Examples are given to demonstrate the performance of the proposed algorithm.     

Locating Human Faces in a Cluttered Scene

In this paper, we present two new schemes for finding human faces in a photograph. The first scheme adopts a distribution-based model approach to face-finding. Distributions of the face and the face-like manifolds are approximated using higher order statistics (HOS) by deriving a series expansion of the density function in terms of the multivariate Gaussian and the Hermite polynomials in an attempt to get a better approximation to the unknown original density function. An HOS-based data clustering algorithm is then proposed to facilitate the decision process. The second scheme adopts a hidden Markov model (HMM) based approach to the face-finding problem. This is an unsupervised scheme in which face-to-nonface and nonface-to-face transitions are learned by using an HMM. The HMM learning algorithm estimates the HMM parameters corresponding to a given photograph and the faces are located by examining the optimal state sequence of the HMM. We present experimental results on the performance of both schemes. A training data base of face images was constructed in the laboratory. The performances of both the proposed schemes are found to be quite good when measured with respect to several standard test face images.     

基于径向基函数神经网络的混沌背景谐波提取

为提高强混沌背景下谐波信号的提取能力,给出混沌系统的单步预测模型,提出了一种新的径向基函数神经网络模型.由混沌吸引子的维数来确定网络的输入,并给出了基于卡尔曼滤波器的动态学习算法,利用学习算法可以在网络训练时同时确定径向基神经网络隐层中心和输出层权值,提高了网络的收敛速度和预测性能.通过对Bossler混沌背景下低信噪比谐波信号的提取进行计算机认真实验,并且实验表明信噪比最低达一27dB时,仍能有效提取出谐波信号,验证了算法的有效性和可行性.     

Noisy texture classification: A higher-order statistics approach

In this paper, a novel texture classification scheme using higher-order statistics (HOS) functions as discriminating features is proposed. It is well known that such statistical parameters are insensitive to additive Gaussian noise. In particular, third-order statistical parameters, i.e. third-order cumulants and bispectrum, are insensitive to any symmetrically distributed noise, and also exhibit the capability of better characterizing non-Gaussian signals. By exploiting these HOS properties, it is possible to devise a robust method for classifying textures affected by noise with different distributions and even with very low signal-to-noise ratios.     

基于径向基函数神经网络的混沌干扰信号检测

为提高强混沌背景下谐波信号的检测能力,提高系统的信噪比,提出了一种在混沌背景噪声中提取正弦信号的RBF神经网络方法。依据混沌吸引子固有的几何特性和混沌系统轨迹点在流形中的演化规律,建立混沌系统的RBF神经网络单步预测模型,改进了网络的学习算法,利用RBF神经网络对输入扰动的敏感,预测出误差信号。分析了在低信噪比下的检测性能。通过对Lorenz流和实际舰船辐射噪声信号中的信号检测进行计算机仿真实验,验证了算法的有效性和可行性,并且实验表明信噪比最低达-40dB时,仍能有效检测出信号。     

An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks

This paper presents a simple sequential growing and pruning algorithm for radial basis function (RBF) networks. The algorithm referred to as growing and pruning (GAP)-RBF uses the concept of "Significance" of a neuron and links it to the learning accuracy. "Significance" of a neuron is defined as its contribution to the network output averaged over all the input data received so far. Using a piecewise-linear approximation for the Gaussian function, a simple and efficient way of computing this significance has been derived for uniformly distributed input data. In the GAP-RBF algorithm, the growing and pruning are based on the significance of the "nearest" neuron. In this paper, the performance of the GAP-RBF learning algorithm is compared with other well-known sequential learning algorithms like RAN, RANEKF, and MRAN on an artificial problem with uniform input distribution and three real-world nonuniform, higher dimensional benchmark problems. The results indicate that the GAP-RBF algorithm can provide comparable generalization performance with a considerably reduced network size and training time.     

Steady-state performance analysis of HOS-based blind adaptive multiuser detection

The blind adaptive multiuser detections based on higher-order statistics (HOS) can obtain higher steady-state decorrelating performance than the conventional linear algorithm under the high SNR condition. However, the closed-form analysis for this steady-state performance is scarce due to the complication of analyzing the nonlinear updates of the adaptive algorithm. An analysis approach based on ordinary differential equation (ODE) method is proposed to get the closed-form excess mean-square error (EMSE) expression of the HOS-based multiuser detections. The simulation and the comparison verify the results of the analysis. Supported by the National Natural Science Foundation of China (Grant No. 60432040), and the Guangxi Natural Science Foundation (Grant Nos. 0731026, 0731025)     

三阶累积量的语音激活检测方法

在电子与通信系统中,传输信道的噪声都可以看作是加性的高斯随机过程,而高斯随机过程的三阶累积量为零,通信系统中传输的语音信号一般是非高斯信号,基于这个原理提出一种语音激活检测算法。在通信系统的接收端,对于被噪声污染了的语音信号,通过计算接收信号的三阶累积量,得到语音信号的累积量,从而可以区分语音和噪声,达到检测出语音信号的目的。仿真结果表明,在通信系统低信噪比的环境下能有效地检测出语音信号。     

Diagnosis of poor control-loop performance using higher-order statistics

Higher-order statistical (HOS) techniques were first proposed over four decades ago. This paper is concerned with higher-order statistical analysis of closed-loop data for diagnosing the causes of poor control-loop performance. The main contributions of this work are to utilize HOS tools such as cumulants, bispectrum and bicoherence to develop two new indices: the non-Gaussianity index (NGI) and the nonlinearity index (NLI) for detecting and quantifying non-Gaussianity and nonlinearity that may be present in regulated systems, and to use routine operating data to diagnose the source of nonlinearity. The new indices together with some graphical plots have been found to be useful in diagnosing the causes of poor performance of control loops. Successful applications of the proposed method are demonstrated on simulated as well as industrial data. This study clearly shows that HOS-based methods are promising for closed-loop performance monitoring.     

非高斯噪声下的回声消除算法研究

回声消除一直是信号处理领域的热门研究方向,其中自适应滤波器是在回声消除问题中最为广泛应用的技术,但自适应滤波算法主要是在基于高斯噪声条件下的应用,而现实环境广泛存在着非高斯的噪声,这严重影响了基于L2范数的自适应噪声滤波算法的噪声消除性能。为解决回声消除方法对非高斯噪声的适用性问题,根据回声路径具有明显的稀疏系统特性,结合比例矩阵的设计思想以及符号算法(SA),提出一种改进的MIPNSA算法。该滤波算法既能很好地适应于不同的背景噪声,同时也在较大程度上增强了对稀疏系统的适应能力。仿真测试结果表明,在高斯噪声和非高斯噪声条件下,本算法比现有的一些算法的回声消除效果更佳。     

基于高阶统计量的压缩宽带频谱盲检测方法

针对认知无线网络中宽带频谱感知受到高速模数转换器(ADC)器件的技术限制,利用压缩感知理论(CS),采用压缩信号处理技术,直接对压缩观测数据进行分析,推导出宽带频谱检测的高阶判决统计量的概率分布特性,并在此基础上提出了一种基于高阶统计量的压缩宽带频谱盲检测算法(HOS-CWSBD).该算法无需任何有关主用户(PU)信号的先验知识、也无需事先重构出原信号就能实现宽带频谱检测.理论分析和仿真结果均表明,与传统的基于压缩感知理论且需要信号重构的压缩频谱感知算法以及基于Nyquist采样数据的非压缩宽带频谱感知算法相比,该算法具有计算复杂度低、感知性能稳定等优点.     

An Information-Theoretic View of Array Processing

The removal of noise and interference from an array of received signals is a most fundamental problem in signal processing research. To date, many well-known solutions based on second-order statistics (SOS) have been proposed. This paper views the signal enhancement problem as one of maximizing the mutual information between the source signal and array output. It is shown that if the signal and noise are Gaussian, the maximum mutual information estimation (MMIE) solution is not unique but consists of an infinite set of solutions which encompass the SOS-based optimal filters. The application of the MMIE principle to Laplacian signals is then examined by considering the important problem of estimating a speech signal from a set of noisy observations. It is revealed that while speech (well modeled by a Laplacian distribution) possesses higher order statistics (HOS), the well-known SOS-based optimal filters maximize the Laplacian mutual information as well; that is, the Laplacian mutual information differs from the Gaussian mutual information by a single term whose dependence on the beamforming weights is negligible. Simulation results verify these findings.     

基于互累积量的有噪独立分量分析方法

针对传统独立分量分析(ICA)方法无噪假设的局限性,提出基于互累积量的有噪ICA方法。考虑含高斯噪声的瞬时混合模型,以观测信号的互累积量组成一系列对称矩阵,以对称矩阵的联合对角化程度为目标函数,采用粒子群优化算法对混合矩阵进行全局寻优。通过寻优得到混合矩阵,将有噪ICA转化为一维欠定ICA,基于奇异值分解法得到源信号的估计。仿真结果表明,与传统ICA方法相比,该方法对混合矩阵的估计精度较高,可以明显提高分离信号的信噪比。     

Optimization-based iterative learning for precise quadrocopter trajectory tracking

Current control systems regulate the behavior of dynamic systems by reacting to noise and unexpected disturbances as they occur. To improve the performance of such control systems, experience from iterative executions can be used to anticipate recurring disturbances and proactively compensate for them. This paper presents an algorithm that exploits data from previous repetitions in order to learn to precisely follow a predefined trajectory. We adapt the feed-forward input signal to the system with the goal of achieving high tracking performance—even under the presence of model errors and other recurring disturbances. The approach is based on a dynamics model that captures the essential features of the system and that explicitly takes system input and state constraints into account. We combine traditional optimal filtering methods with state-of-the-art optimization techniques in order to obtain an effective and computationally efficient learning strategy that updates the feed-forward input signal according to a customizable learning objective. It is possible to define a termination condition that stops an execution early if the deviation from the nominal trajectory exceeds a given bound. This allows for a safe learning that gradually extends the time horizon of the trajectory. We developed a framework for generating arbitrary flight trajectories and for applying the algorithm to highly maneuverable autonomous quadrotor vehicles in the ETH Flying Machine Arena testbed. Experimental results are discussed for selected trajectories and different learning algorithm parameters.     

基于解相关变步长的改进型语音增强算法

复杂环境中噪声干扰严重影响语音信号的质量,无法正确传达语义,因此语音增强处理十分必要。传统语音增强技术存在适应性差、输入信号高度相关时收敛速度慢等问题。综合变步长最小均方(VSSLMS)算法与解相关的优点,提出了一种改进的语音增强算法,优化自适应滤波算法中步长的大小和权矢量的更新方向,提高语音降噪收敛速度。同时算法引入了连续块处理理论归一化权矢量,以提高其在嵌入式系统实现上的稳定性。仿真测试表明该算法收敛速度快、跟踪性能强,能有效去除强噪语音信号中的噪声,提高语音的清晰度与可懂度。     

多载波CDMA子载波频率盲估计算法及其性能分析*

针对频谱感知和多载波CDMA信号解调的实际应用,根据多载波CDMA信号的循环平稳特性,提出了一种利用高阶循环累积量估计多载波CDMA信号子载波频率的方法。由于高阶循环累积量可以有效地抑制平稳噪声和非平稳高斯噪声,通过理论分析可以证明在上述噪声背景下,子载波采用BPSK调制的多载波CDMA信号的四阶循环累积量仅在循环频率为子载波频率处存在,可以通过检测此循环频率来实现子载波的估计。考虑到多载波CDMA信号发端可以采用不同的窗函数以降低频谱泄露,以常见的几种窗函数为例进行了算法仿真,发现本算法对窗函数的变化不     

滤波器组自适应语音增强方法

双麦克风噪声抵消应用中,由于交叉串的存在,传统自适应算法降噪性能受到很大的影响。为了提高双麦克风算法降噪性能,使用两级自适应滤波系统消除交叉串扰问题。为提高自适应滤波器收敛性能,采用主从结构LMS算法自适应调节步长因子。同时为了适合窄带处理算法,将输入信号进行子带分析预处理,对每个子带独立进行抗交叉串绕自适应处理,将各子带增强信号合并得到增强语音信号。实验结果表明,该方消噪量大,语音损伤小,语音增强效果显著。     

基于带有噪声输入的稀疏高斯过程的人体姿态估计

高斯过程回归（Gaussian process regression，GPR）是一种广泛应用的回归方法，可以用于解决输入输出均为多元变量的人体姿态估计问题.计算复杂度是高斯过程回归的一个重要考虑因素，而常用的降低计算复杂度的方法为稀疏表示算法.在稀疏算法中，完全独立训练条件（Fully independent training conditional，FITC）法是一种较为先进的算法，多用于解决输入变量彼此之间完全独立的回归问题.另外，输入变量的噪声问题是高斯过程回归的另一个需要考虑的重要因素.对于测试的输入变量噪声，可以通过矩匹配的方法进行解决，而训练输入样本的噪声则可通过将其转换为输出噪声的方法进行解决，从而得到更高的计算精度.本文基于以上算法，提出一种基于噪声输入的稀疏高斯算法，同时将其应用于解决人体姿态估计问题.本文实验中的数据集来源于之前的众多研究人员，其输入为从视频序列中截取的图像或通过特征提取得到的图像信息，输出为三维的人体姿态.与其他算法相比，本文的算法在准确性，运行时间与算法稳定性方面均达到了令人满意的效果.     

基于增益字典查询的语音增强算法

对于基于统计模型的语音增强算法,不同分布模型对应于不同的增益函数,由于语音信号的不确定性,没有一种分布函数能准确对语音和噪声谱的分布建模,因此任何一种固定的统计模型均会存在一定的误差。所以提出一种增益字典查询的语音增强算法,该算法通过采用对数谱失真准则对一个语音噪声库进行增益的训练,得到一个增益的字典,其中输入为先验信噪比和后验信噪比的估计值。最后采用ITU-T P.826 PESQ、分段信噪比、总信噪比和对数谱失真对该算法进行了测试,并与基于高斯分布模型、拉普拉斯分布模型的算法进行了对比。实验结果表明,该算法无论在非平稳噪声还是平稳噪声环境下都比其他几种算法增强效果好,且音乐噪声和残留背景噪声也可以得到很好的抑制。