一种新的高动态GPS载波跟踪算法

提出了基于无迹粒子滤波（UPF）算法的高动态GPS载波跟踪环路,仿真分析了该方案在高斯噪声和非高斯噪声环境下对高动态GPS信号的跟踪性能,并与分别基于扩展卡尔曼滤波（EKF）、无迹卡尔曼滤波（UKF）、粒子滤波（PF）及扩展卡尔曼粒子滤波（EPF）这四种算法的载波跟踪环路进行了性能对比。仿真结果表明,基于UPF估计器的载波跟踪环路在高动态、弱信号以及非高斯噪声环境下具有优越的跟踪性能,既可以提高跟踪精度,又解决了非高斯噪声干扰问题。通过模拟实验验证了该方案的有效性。     

非高斯噪声背景下小波阈值算法分析

针对小波阈值算法以高斯噪声为研究背景的局限性,为解决硬阈值函数不连续和软阈值函数估计小波系数和分解小波系数存在恒定偏差的问题,在非高斯噪声背景下提出一种新的小波阈值算法。新阈值函数从Garrote阈值改进而来,引入了高阶幂函数。该算法首先对加入一类非高斯噪声的信号进行小波分解,然后根据新的阈值函数对每层高频小波系数进行量化,最后用小波分解的低频系数和处理过的高频系数重构信号。在非高斯噪声背景下进行的仿真结果表明,新阈值函数去噪相对于软阈值、硬阈值、两类改进阈值以及Garrote阈值在信噪比和最小均方误差上都得到了改善。     

Stochastic system identification with noisy input using cumulantstatistics

Addresses the problem of estimating the parameters of stochastic linear systems when the measurements of the system input as well as the system output are noise contaminated. It is assumed that the input is non-Gaussian and the noises are Gaussian. The square root of the magnitude of the fourth cumulant of a generalized error signal is proposed as a performance criterion for parameter estimation. An optimization algorithm is presented. Strong consistency of the proposed parameter estimators is proved under certain sufficient conditions. Both single-input single-output and multiple-input multiple-output cases are investigated. Finally, simulation results are presented to illustrate the proposed approach     

Robust identification of nonlinear objects with the help of an evolving radial basis network

The problem of neural network-based robust identification of nonlinear dynamic objects in the presence of non-Gaussian noise is considered. To solve this problem, a radial basis network was chosen whose structure is specified and training is provided with the help of a genetic algorithm. The simulation results are presented that confirm the efficiency of the proposed approach.     

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.     

基于小波包变换的Robust-M检测器

利用小波包分析将信号变换到其小波包系数域上，通过研究平稳随机噪声在各子空间上小波包系数的统计特性，结合Robust检测理论的相关结论，对非高斯噪声下的信号检测问题建立了统一的框架，并提出了一种新的检测算法。仿真实验表明，此算法不仅具有良好的鲁棒性和广泛的适用性，而且能够更为充分地利用噪声统计分布信息，从而有效地提高检测效率。     

An extension of the stochastic linear regulator problem

The stochastic linear regulator problem is extended to cover the case of non-Gaussian white noise. Using martingale theory, it is proved that the linear optimal regulator is also optimal for non-Gaussian white noise.     

Harmonic retrieval by period blind source extraction method: Model and algorithm

A frequently encountered problem in signal processing field is harmonic retrieval in additive colored Gaussian or non-Gaussian noise, especially when the frequencies of the harmonic signals are closely spaced in frequency domain. The purpose of this paper is to develop novel harmonic retrieval algorithm based on blind source extraction (BSE) method from linear mixtures of harmonic signals using only one observed channel signal. First, we establish the blind source separation (BSS) based harmonic retrieval model in additive noise using the only one observed channel, at the same time, the fundamental principle of BSE based harmonics retrieval algorithm is analyzed in detail. Then, based on the established harmonic BSS model, we propose a BSE approach to the harmonic retrieval using the concept of period BSE method, as a result, the harmonic retrieval algorithm using only one channel mixed signals is derived. Simulation results show that the proposed algorithm is able to separate the harmonic source signals and yield ideal performance.     

基于IEWT-CCE的电动充电站主变流器开路故障检测

直流充电站变流器开关器件的开路故障严重影响电站安全运行.考虑充电站运行中不可避免存在着非高斯测量噪声,提出一种基于尺度空间理论的经验小波变换(EWT)和循环熵(CCE)的变流器IGBT开路故障检测方法.首先,分析非高斯噪声对变流器IGBT开路故障的影响;接着,利用改进EWT方法分解IGBT开路故障时交流侧电流信号,通过...     

An adaptive robustizing approach to kalman filtering

The performance of a linear Kalman filter will degrade when the dynamic noise is not Gaussian. A robust Kalman filter based on the m-interval polynomial approximation (MIPA) method for unknown non-Gaussian noise is proposed. Two situations are considered: (a) the state is Gaussian and the observation noise is non-Gaussian; (b) the state is non-Gaussian and the observation noise is Gaussian. It is shown, as compared with other non-Gaussian filters, the MIPA Kalman filter is computationally feasible, unbiased, more efficient and robust. For the scalar model, Monte Carlo simulations are given to demonstrate the ideas involved.     

Suppression of Impulsive Noise in FM via Rank Order Processing

Suppression of impulsive non-Gaussian noise in FM, via rank order signal processing methods, appears feasible in circumstances whose precise limits, particularly in terms of bandwidth and levels of nonimpulsive noise, remain to be determined. The methods are related to median filtering, but are elaborated in the sense of admitting more operations on the signal values. The application is feasible because demodulatian of FM requires only zero crossing information, and because rank order processors can be defined whose root signals have the same sign changes as the FM signals admitted.     

认知无线电中非高斯噪声下主用户信号的协作检测方法

本文研究了非高斯噪声情况下主用户的频谱检测问题,采用混合高斯模型拟合了非高斯噪声背景,并采用矩估计方法对混合高斯模型的混合系数等参数进行了估计。在此基础上,将混合高斯Rao检测方法应用于主用户的频谱检测,推导了混合高斯Rao检测的检测统计量和检测性能公式,分析比较了混合高斯Rao检测与高斯Rao检测的性能。此外,建立了多用户协作的检测模型并推导了基于改进的OR准则的协作检测性能公式。通过理论分析和蒙特卡罗仿真说明了在虚警概率一定的情况下,基于混合高斯Rao检测的方法能有效地提高非高斯噪声下主用户的检测性能。     

多变量ARMA信号自校正最优去卷平滑估计方法及其渐近稳定性*

本文讨论信号去卷估计问题。运用新息理论和射影方法，基于ＡＲＭＡ新息模型设计多变量ＡＲＭＡ信号最优去卷平滑器，讨论了平滑器的渐近稳定性。在信号模型及噪声统计未知时，通过在线辨识ＡＲＭＡ新息模型提出了ＡＲＭＡ信号自校正去卷平滑器。     

一种图像增强新方法

提出了一种四树复小波包变换域层内层间系数相关性图像增强新方法。该方法利用四树复小波包变换具有移不变性、良好方向选择性和对高频信号的细致分析能力,把含噪图像分解成低频逼近子图和若干高频方向子图;在保留低频逼近子图复系数不变的同时,充分利用变换域信号复系数层间相关性强和噪声复系数层间相关性弱的特点,采用非高斯双变量模型对每一个方向子图复系数进行降噪处理。同时考虑图像的弱边缘在变换域某些方向子图内复系数值较大,而在其他方向子带内其值较小的特点,甄别出弱边缘点对应的复系数并进行增强处理。实验结果表明,无论是PSNR指标,还是在视觉效果上,该方法的增强性能均好于传统的双树复小波变换去噪、四树复小波包变换去噪和小波域高斯尺度混合模型去噪,在有效抑制噪声的同时,具有很好的图像弱边缘增强和细节保护能力。     

基于MCC的鲁棒高阶CKF在组合导航中的应用

针对高阶容积卡尔曼滤波器在非高斯噪声情况下滤波精度下降的问题,提出了一种新的基于Maximum Correntropy Criterion（MCC）的鲁棒高阶容积卡尔曼滤波算法。考虑到高阶容积规则可以较好地解决非线性问题,在高阶容积滤波的基础上,结合统计线性回归模型对量测更新过程进行重构,利用MCC估计算法实现状态的量测更新,同时解决了系统的非线性和非高斯问题。将所提算法应用到SINS/GPS组合导航系统中,仿真结果表明,核宽的选取对算法的滤波性能有较大的影响,在高斯混合噪声条件下,所提算法相比传统高阶容积卡尔曼滤波算法具有更强的鲁棒性和更高的滤波精度。     

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。     

Higher-Order Asymptotics of Mutual Information for Nonlinear Channels with Non-Gaussian Noise

Nonlinear channels with non-Gaussian noise where the transmitted signal is a random function of the input signal are considered. Under some assumptions on smoothness and the behavior of tails of the noise density function, higher-order asymptotics of the mutual information between the input and output signals in such channels is obtained, as the mean power of the input signal (or, equivalently, the signal-to-noise ratio) goes to zero.     

An innovations approach to least-squares estimation--Part III: Nonlinear estimation in white Gaussian noise

In Parts I and II of this paper, we presented the innovations approach to linear least-squares estimation in additive white noise. In the present paper, we show how to extend this technique to the nonlinear estimation (filtering and smoothing) of non-Gaussian signals in additive white Gaussian noise. The use of the innovations allows us to obtain formulas and simple derivations that are remarkably similar to those used for the linear case thereby distinguishing clearly the essential points at which the nonlinear problem differs from the linear one.     

α稳定分布下信号的维格纳分布分析方法

研究分数低阶α稳定分布(FLOA)噪声下的信号仅从频域分析是不够的,需要考虑时—频域的二维信息。从现有的时—频分析方法发现,该方法是假设信号的背景噪声服从高斯分布,对FLOA这类非高斯分布噪声下信号的时—频分析性能退化严重。针对这一问题,在基于频域的FLOA分析理论上,将频域推广到时—频域的维格纳分布(WVD),提出了分数低阶WVD分析方法。计算机仿真测试结果表明,该新的WVD分析方法能在时—频域有效分析出FLOA的时—频分布特征,成功地使FLOA分析方法从频域拓展到时—频域。     

基于FastICA的雷达弱目标检测前跟踪方法研究

研究了强噪声干扰下的雷达弱目标检测及跟踪问题。针对信号与噪声干扰之间的相互独立性,提出了一种基于FastICA的弱目标检测前跟踪(TBD)算法。该算法的关键为对分离后的信号进行能量分配,以及求解所构建的一个多目标规划问题,进而实现匹配回波信号的能量积累。仿真实验结果表明,在负信噪比的情况下,无论是高斯噪声还是非高斯噪声,该方法均可以实现检测前跟踪的目的;用能量积累过程中记录的目标状态信息为观测值,则可以通过滤波估计的方法最终实现对目标的稳健跟踪。该方法为弱小目标的跟踪检测技术提供了一种新的思路。