基于HFM和感知后滤波器的语音增强

研究了只能获得带噪信号的情况下的语音增强问题。将语音信号看作由高斯噪声激励的自回归(AR)过程,观测噪声为加性高斯白噪声,把信号转化为状态空间模型。首先用隐马尔可夫模型(HMM)估计AR参数和噪声的方差作为卡尔曼滤波器初值,估计信号作为参数估计的中间值给出,然后将估计信号通过一个感知滤波器平滑以消除残余噪声。仿真结果表明该算法有良好的性能。     

EM-based recursive estimation of channel parameters

Recursive (online) expectation-maximization (EM) algorithm along with stochastic approximation is employed in this paper to estimate unknown time-invariant/variant parameters. The impulse response of a linear system (channel) is modeled as an unknown deterministic vector/process and as a Gaussian vector/process with unknown stochastic characteristics. Using these models which are embedded in white or colored Gaussian noise, different types of recursive least squares (RLS), Kalman filtering and smoothing and combined RLS and Kalman-type algorithms are derived directly from the recursive EM algorithm. The estimation of unknown parameters also generates new recursive algorithms for situations, such as additive colored noise modeled by an autoregressive process. The recursive EM algorithm is shown as a powerful tool which unifies the derivations of many adaptive estimation methods     

Adaptive nonlinear filters for narrow-band interference suppressionin spread-spectrum CDMA systems

This paper presents a novel nonlinear filter and parameter estimator for narrow band interference suppression in code division multiple access spread-spectrum systems. As in the article by Rusch and Poor (1994), the received sampled signal is modeled as the sum of the spread-spectrum signal (modeled as a finite state independently identically distributed (i.i.d.) process-here we generalize to a finite state Markov chain), narrow-band interference (modeled as a Gaussian autoregressive process), and observation noise (modeled as a zero-mean white Gaussian process). The proposed algorithm combines a recursive hidden Markov model (HMM) estimator, Kalman filter (KF), and the recursive expectation maximization algorithm. The nonlinear filtering techniques for narrow-band interference suppression presented in Rusch and Poor and our proposed HMM-KF algorithm have the same computational cost. Detailed simulation studies show that the HMM-KF algorithm outperforms the filtering techniques in Rusch and Poor. In particular, significant improvements in the bit error rate and signal-to-noise ratio (SNR) enhancement are obtained in low to medium SNR. Furthermore, in simulation studies we investigate the effect on the performance of the HMM-KF and the approximate conditional mean (ACM) filter in the paper by Rusch and Poor, when the observation noise variance is increased. As expected, the performance of the HMM-KF and ACM algorithms worsen with increasing observation noise and number of users. However, HMM-KF significantly outperforms ACM in medium to high observation noise     

Design of Gaussian Approximate Filter and Smoother for Nonlinear Systems with Correlated Noises at One Epoch Apart

In this study, the authors investigate the filtering and smoothing problems of nonlinear systems with correlated noises at one epoch apart. A pseudomeasurement equation is firstly reconstructed with a corresponding pseudomeasurement noise, which is no longer correlated with the process noise. Based on the reconstructed measurement model, new Gaussian approximate (GA) filter and smoother are derived, from which Kalman filter and smoother can be obtained for linear systems. For nonlinear systems, different GA filters and smoothers can be developed through utilizing different numerical methods for computing Gaussian-weighted integrals involved in the proposed solution. Numerical examples concerning univariate nonstationary growth model, passive ranging problem, and target tracking show the efficiency of the proposed filtering and smoothing methods for nonlinear systems with correlated noises at one epoch apart.     

自适应非线性GM-PHD滤波及在无源跟踪中的应用

首先针对无源传感器目标跟踪中的非线性问题,将高斯-厄米特求积分规则运用于高斯混合概率假设密度滤波,提出一种求积分卡尔曼概率假设密度滤波。其次,针对未知时变过程噪声,将基于极大后验估计原理的噪声估计器运用到概率假设密度滤波中,同时依据目标状态一步预测与状态滤波结果之间的残差,提出一种对滤波发散情况判断和抑制的算法。最后通过无源传感器双站跟踪仿真表明:相较于已有的非线性高斯混合概率假设密度滤波,所提算法有更高的精度,并且在未知时变噪声环境中具有较好跟踪效果。     

粒子滤波在目标跟踪算法中的应用研究

针对非高斯、强噪声背景下的高机动目标实施跟踪时,卡尔曼滤波、扩展卡尔曼滤波等算法将出现滤波精度下降甚至发散现象。粒子滤波方法作为一种基于贝叶斯估计的非线性滤波算法,在处理非高斯非线性时变系统的参数估计和状态滤波问题方面有独到的优势。以目标跟踪问题为背景,将粒子滤波与卡尔曼滤波算法进行了对比研究。     

修正Kalman滤波多带UWB信道估计改进方法

针对多带超宽带(UWB)系统中修正Kalman滤波算法复杂度高的缺陷,提出一种低复杂度的修正Kalman滤波改进方法。该方法中UWB信道采用自回归模型(AR)建模,利用导频跟踪时变信道衰减因子,通过Kalman滤波和频域分段最小均方误差(MMSE)算法同时跟踪信道的时域相关性和频域相关性,提高了系统性能,降低了计算复杂度。仿真结果表明,和修正的Kalman滤波方法相比,在估计精度损失很小的情况下,所提方法极大降低了计算复杂度,提高了系统整体的估计性能。     

A DS-CDMA tracking mode receiver with joint channel/delayestimation and MMSE detection

A tracking mode receiver for asynchronous direct-sequence CDMA is presented based on the extended Kalman filter (EKF). The EKF jointly estimates the delays and multipath coefficients of the received CDMA waveform, and provides a modified minimum mean-square error (MMSE) estimate of the user data (MMSE-EKF). In order to obtain a practical algorithm, each user signal is tracked individually, with the remaining users modeled as colored Gaussian noise. However, the EKFs are coupled through the multiple access interference (MAI) covariance estimates. In order to obtain meaningful performance measures, approximate worst-case undesired user delays that minimize the desired user SNR and delay estimation Cramer-Rao bound are obtained. It is shown that such worst-case delays can be efficiently computed using the alternating maximization (A-M) algorithm. The resulting bit error rate (BER) performance of the MMSE-EKF tracking receiver is evaluated through a combination of simulation and analysis. The mean-time to lose lock (MTTLL) for a genie-aided EKF delay estimator is also obtained using the A-M computed delays     

基于最大熵Kalman滤波的机载SINS自标定技术

针对在机载捷联惯导系统(SINS)自标定过程中,量测噪声呈非高斯分布,导致经典Kalman滤波性能降低的问题,该文提出了基于最大熵Kalman滤波(MCKF)的机载SINS自标定技术。该方法采用最大相关熵准则(MCC)替代经典Kalman滤波的最小均方误差准则,有效利用信号的高阶矩信息,并将其应用于机载SINS自标定系统中。仿真结果表明,在非高斯噪声条件下,该方法能够估计出机载SINS待标定参数,且算法的鲁棒性和误差项估计精度均优于经典Kalman滤波,具有一定的工程应用价值。     

多传感器分布式融合白噪声反卷积滤波器

基于Kalman滤波方法和白噪声估计理论,在按矩阵加权线性最小方差最优融合准则下,提出了带ARMA有色观测噪声系统的多传感器分布式融合白噪声反卷积滤波器,其中推导出用Lyapunov方程计算最优加权的局部估计误差互协方差公式。与单传感器情形相比,可提高融合估值器精度。它可应用于石油地震勘探信号处理。一个三传感器分布式融合Bernoulli-Gauss白噪声反卷积平滑器的仿真例子说明了其有效性。     

Harmonic retrieval using higher order statistics: a deterministicformulation

Given a single record, the authors consider the problem of estimating the parameters of a harmonic signal buried in noise. The observed data are modeled as a sinusoidal signal plus additive Gaussian noise of unknown covariance. The authors define novel higher order statistics-referred to as “mixed” cumulants-that can be consistently estimated using a single record and are insensitive to colored Gaussian noise. Employing fourth-order mixed cumulants, they estimate the sinusoid parameters using a consistent, nonlinear matching approach. The algorithm requires an initial estimate that is obtained from a consistent, linear estimator. Finally, the authors examine the performance of the proposed method via simulations     

改进卡尔曼滤波算法在语音信号处理中的应用

针对传统卡尔曼滤波算法在加性噪声影响下的语音信号处理中要先对被观测系统的状态进行统计估值和计算量很大的问题．提出了改进卡尔曼滤波算法。该算法不用预先估计噪声、驱动项以及语音模型参数,可直接得到增强的语音信号。通过仿真实验证明,该方法在减少计算量的同时,有效地消除了加性噪声,并能保持较好的语音可懂度。     

Amended Kalman Filter for Maneuvering Target Tracking

The conventional Kalman filter (KF) which uses the current measurement to estimate the current state is a posterior estimation.KF is identified as the optimal estimation in linear models with Gaussian noise.However,the performance of KF with incomplete information may be degraded or diverged.In order to improve the performance of KF,an Amended KF (AKF) is proposed by using more posterior measurements.The principle,derivation and recursive process of AKF are presented.The differences among Kalman smoother,adaptive fading method and AKF are analyzed.The simulation results of target tracking with different covariance of motion model indicate the high precision and robustness of AKF.     

A Maximum Likelihood Method for Estimating EEG Evoked Potentials

The problem of EEG evoked potential (EP) estimation is basically one of estimating a transient signal embedded in nonstationary mostly additive noise; and as such it is well suited to a nonstationary estimation approach utilizing, for example, the Kalman filter. The method presented in this paper is based on a model of the EEG response which is assumed to be the sum of the EP and independent correlated Gaussian noise representing the spontaneous EEG activity. The EP is assumed to vary in both shape and latency; the latter is assumed to be governed by some unspecified probability density; and no assumption on stationarity is needed for the noise. With the model described in state-space form, a Kalman filter is constructed, and the variance of the innovation process is derived; a maximum likelihood solution to the EP estimation problem is then obtained via this innovation process. The method was tested on simulated as well as real EEG data.     

在脉冲噪声环境中用于快衰信道估计的改进型算法

该文分析了在存在噪声干扰的情况下,进行估计快衰信道的方法。在无线通信系统中,快衰信道可以采用AR(Auto-Regressive)模型进行预测,而LS (Least Square)算法和自适应Kalman滤波器可以分别对AR模型的参数和信道的冲激响应进行估计,但是这两种算法对噪声干扰非常敏感。该文提出改进型的RLM算法和Kalman 滤波器,并在存在噪声的情况下,使用它们并行对AR参数和信道的冲激响应进行联合估计。仿真结果显示:相比于传统的算法,改进后的算法在联合估计信道时,提高了抵抗大脉冲干扰的能力,加快了待估的参数的收敛速度。     

A fast recursive algorithm for the maximum likelihood estimation ofthe parameters of a periodic signal

Describes a new recursive algorithm for the estimation of the parameters of a periodic signal in additive Gaussian white noise. These parameters are the period, and the complex amplitudes of the harmonics present. The proposed algorithm is based on recursive maximum likelihood (ML) algorithms for incomplete data as described by Titterington (1985) and others. These algorithms are of complexity O(NM), where N is the number of harmonics, and M is the signal length. The performance of the method is compared to that of the extended Kalman filter with the aid of simulations     

Nonuniform filter bank design with noises

This work solves the signal reconstruction problem involving nonuniform filter bank systems with rational decimation factors and noise. Three main nonuniform filter bank systems, i.e., filter-block decimator (FBD) structure, upsampler-filter-downsampler (UFD) structure, and tree structure, are included in this study. According to different operating conditions, two different signal reconstruction problems for nonuniform filter bank systems with noise under the unknown but identifiable input signal model and the unknown input signal model are discussed, respectively. At the first stage, a unified block state space model for different nonuniform filter bank systems with noise is developed. Then, by incorporating the identified input signal model with this unified state space model and appropriate choice of the augmented state vector, the signal reconstruction problem is reduced to an equivalent state estimation problem for resulting augmented systems if the input signal is identifiable. If the input signal is lacking in modeling, the signal reconstruction is discussed from the minimax estimation point of view. Two state estimation techniques involving robust Kalman filtering and H_∞ filtering are employed, respectively, to treat the signal reconstruction problem of nonuniform filter bank systems according to different a priori knowledge of the input signal. Finally, several numerical examples are presented to illustrate the proposed algorithms and exhibit the performances     

Recursive estimation of images using non-Gaussian autoregressivemodels

We consider recursive estimation of images modeled by non-Gaussian autoregressive (AR) models and corrupted by spatially white Gaussian noise. The goal is to find a recursive algorithm to compute a near minimum mean square error (MMSE) estimate of each pixel of the scene using a fixed lookahead of D rows and D columns of the observations. Our method is based on a simple approximation that makes possible the development of a useful suboptimal nonlinear estimator. The algorithm is first developed for a non-Gaussian AR time-series and then generalized to two dimensions. In the process, we draw on the well-known reduced update Kalman filter (KF) technique of Woods and Radewan (1977) to circumvent computational load problems. Several examples demonstrate the non-Gaussian nature of residuals for AR image models and that our algorithm compares favorably with the Kalman filtering techniques in such cases.     

Estimation of noisy telegraph processes: Nonlinear filtering versus nonlinear smoothing (Corresp.)

In the estimation problem of a two-state stationary Markov process with Gaussian white noise added, the optimal smoother is a two-filter smoother. In a special case, the performance of the optimal nonlinear filter and smoother is evaluated analytically. Some asymptotic results are also derived.     

Wavelet-domain filtering for photon imaging systems

Many imaging systems rely on photon detection as the basis of image formation. One of the major sources of error in these systems is Poisson noise due to the quantum nature of the photon detection process. Unlike additive Gaussian white noise, the variance of Poisson noise is proportional to the underlying signal intensity, and consequently separating signal from noise is a very difficult task. In this paper, we perform a novel gedankenexperiment to devise a new wavelet-domain filtering procedure for noise removal in photon imaging systems. The filter adapts to both the signal and the noise, and balances the trade-off between noise removal and excessive smoothing of image details. Designed using the statistical method of cross-validation, the filter is simultaneously optimal in a small-sample predictive sum of squares sense and asymptotically optimal in the mean-square-error sense. The filtering procedure has a simple interpretation as a joint edge detection/estimation process. Moreover, we derive an efficient algorithm for performing the filtering that has the same order of complexity as the fast wavelet transform itself. The performance of the new filter is assessed with simulated data experiments and tested with actual nuclear medicine imagery.