Adaptive filter for event-related bioelectric signals using animpulse correlated reference input: comparison with signal averagingtechniques

Many bioelectric signals result from the electrical response of physiological systems to an impulse that can be internal (ECG signals) or external (evoked potentials). In this paper an adaptive impulse correlated filter (AICF) for event-related signals that are time-locked to a stimulus is presented. This filter estimates the deterministic component of the signal and removes the noise uncorrelated with the stimulus, even if this noise is colored, as in the case of evoked potentials. The filter needs two inputs: the signal (primary input) and an impulse correlated with the deterministic component (reference input). We use the LMS algorithm to adjust the weights in the adaptive process. First, we show that the AICF is equivalent to exponentially weighted averaging (EWA) when using the LMS algorithm. A quantitative analysis of the signal-to-noise ratio improvement, convergence, and misadjustment error is presented. A comparison of the AICF with ensemble averaging (EA) and moving window averaging (MWA) techniques is also presented. The adaptive filter is applied to real high-resolution ECG signals and time-varying somatosensory evoked potentials.     

An Improved Adaptive Predictor in DPCM Based on the Kalman Filter and Its Application to Handwriting Signal Encoding

A method of handwriting signal encoding based on adaptive linear predictive coding (ALPC) is studied. The ALPC is a form of DPCM which uses a sequentially adaptive predictor in which a sequential estimation algorithm is used to update predictor coefficients. To improve the estimates of the predictor coefficients in the presence of quantization noise, Kalman filtering has been investigated for its feasibility. This results in improvements of not only the estimation of the predictor coefficients, but the signal-to-quantization-noise ratio (SNR) of the signals reconstructed at the receiver as well. Computer simulations have verified that the ALPC system employing the Kalman filter promises high performance and feasibility at the rate of 192 bits/s when applied to handwriting signal encoding.     

On-line estimation of signal and noise parameters with application to adaptive Kalman filtering

This paper presents two algorithms for on-line estimation of the optimal gain of the Kalman filter applied to sensor signals when the signal-to-noise ratio is unknown. First-order spectra of a pure signal and colored measurement noise have been assumed. The proposed adaptive Kalman filtering algorithms have been tested for various spectra of the pure signal and noise, and for various signal-to-noise ratios. The effect of the length of an adaptation step and a sampling frequency on the mean square errors of the pure signal estimation has also been examined. Although the test have been performed for stationary signals, the algorithms presented can also be used successfully for time-varying sensor signals when the signal-to-noise ratios vary very slowly in comparison with the length of the adaptation step.The results are helpful for designers who synthesize optimal linear digital filters for sensor signals with first-order spectra and colored measurement noise. The estimation error curves presented enable designers to determine the noise reduction attainable for particular applications of the adaptive Kalman filtering algorithms.     

Iterative and sequential algorithms for multisensor signalenhancement

In problems of enhancing a desired signal in the presence of noise, multiple sensor measurements will typically have components from both the signal and the noise sources. When the systems that couple the signal and the noise to the sensors are unknown, the problem becomes one of joint signal estimation and system identification. The authors specifically consider the two-sensor signal enhancement problem in which the desired signal is modeled as a Gaussian autoregressive (AR) process, the noise is modeled as a white Gaussian process, and the coupling systems are modeled as linear time-invariant finite impulse response (FIR) filters. The main approach consists of modeling the observed signals as outputs of a stochastic dynamic linear system, and the authors apply the estimate-maximize (EM) algorithm for jointly estimating the desired signal, the coupling systems, and the unknown signal and noise spectral parameters. The resulting algorithm can be viewed as the time-domain version of the frequency-domain approach of Feder et al. (1989), where instead of the noncausal frequency-domain Wiener filter, the Kalman smoother is used. This approach leads naturally to a sequential/adaptive algorithm by replacing the Kalman smoother with the Kalman filter, and in place of successive iterations on each data block, the algorithm proceeds sequentially through the data with exponential weighting applied to allow adaption to nonstationary changes in the structure of the data. A computationally efficient implementation of the algorithm is developed. An expression for the log-likelihood gradient based on the Kalman smoother/filter output is also developed and used to incorporate efficient gradient-based algorithms in the estimation process     

基于小波变换与形态学运算的ECG自适应滤波算法

针对ECG信号常用滤波算法存在的缺陷,提出了基于小波变换与形态学运算的自适应滤波新算法。该算法利用形态学滤波器去除基线漂移信号,用小波滤波器去除高频干扰信号,并将这两部分所得到的心电噪声分量作为自适应滤波器的参考输入信号,利用自适应滤波器调整对含噪ECG信号进行滤波处理。最后,经实验验证了本文算法的有效性。     

小波变换域估计观测噪声方差的Kalman滤波算法及其在数据融合中的应用

众所周知,卡尔曼滤波的成功应用需要事先准确知道观测噪声的统计特性.本文首先简要分析了不准确的观测噪声统计特性对卡尔曼滤波性能的影响,然后利用小波变换可以实时分离信号和噪声的特性,提出了一种在未知观测噪声条件下的卡尔曼滤波算法,该算法可以实时跟踪观测噪声的变化,即实现了对观测噪声方差的实时估计,从而解决了在未知观测噪声的条件下卡尔曼滤波失效问题.最后讨论了提出的方法在信息融合中的应用,仿真结果证明了本文方法的有效性和实用性.     

Applications of adaptive filtering to ECG analysis: noisecancellation and arrhythmia detection

Several adaptive filter structures are proposed for noise cancellation and arrhythmia detection. The adaptive filter essentially minimizes the mean-squared error between a primary input, which is the noisy ECG, and a reference input, which is either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Different filter structures are presented to eliminate the diverse forms of noise: baseline wander, 60 Hz power line interference, muscle noise, and motion artifact. An adaptive recurrent filter structure is proposed for acquiring the impulse response of the normal QRS complex. The primary input of the filter is the ECG signal to be analyzed, while the reference input is an impulse train coincident with the QRS complexes. This method is applied to several arrhythmia detection problems: detection of P-waves, premature ventricular complexes, and recognition of conduction block, atrial fibrillation, and paced rhythm.     

模糊自适应滤波在捷联惯导初始对准中的应用

传统卡尔曼滤波应用于捷联惯导初始对准中由于模型参数、噪声的统计特性不确定,影响估计效果.而模糊自适应卡尔曼滤波能按照模糊推理原理逐步校正系统的观测噪声协方差阵,具体实现是通过观察残差的理论值是否接近于其实际值,系统调整观测噪声协方差的加权以达到修正观测噪声协方差阵的目的,进而提高系统的对准效率.在噪声统计特性未知时,比较了常规卡尔曼滤波与模糊自适应卡尔曼滤波在初始对准中的应用效果.仿真结果表明,这种算法能有效提高系统的滤波效果,是一种较理想的初始对准滤波方法.     

Speed estimation of an induction motor drive using an optimizedextended Kalman filter

This paper presents a novel method to achieve good performance of an extended Kalman filter (EKF) for speed estimation of an induction motor drive. A real-coded genetic algorithm (GA) is used to optimize the noise covariance and weight matrices of the EKF, thereby ensuring filter stability and accuracy in speed estimation. Simulation studies on a constant V/Hz controller and a field-oriented controller (FOC) under various operating conditions demonstrate the efficacy of the proposed method. The experimental system consists of a prototype digital-signal-processor-based FOC induction motor drive with hardware facilities for acquiring the speed, voltage, and current signals to a PC. Experiments comprising offline GA training and verification phases are presented to validate the performance of the optimized EKF     

可穿戴心电信号监测中运动伪影消除技术研究

心电图(ECG)是心脏疾病诊断最有效的工具。噪声的去除和Q波、R波、S波的提取是心电信号检测中的两大主题。本文使用Savitzky-Golay滤波器对人体在弯腰、走路、坐下-站起等运动状态下采集的心电信号进行分析,去除信号中的基线漂移和运动伪影,并对滤波后信号的Q波、R波和S波进行检测。通过将本文提出的滤波方式与卡尔曼滤波、小波分解就时间复杂度和功率谱密度两个参数进行对比分析,评估Savitzky-Golay滤波器在心电信号中运动伪影去除的优势。实验结果表明,Savitzky-Golay滤波器能更加有效地适应心电信号的变化,有效地去除心电信号中的噪声,并且最大限度保持心电波形的形状和波峰。      

New adaptive beamforming algorithm employing forward/backwardaveraging and signal enhancement schemes

We introduce the new adaptive beamforming algorithm which improves the performance of an adaptive antenna array system through a forward/backward averaging scheme of the post-correlation signal vector and a signal enhancement scheme using Hermitian Toeplitzation of an array covariance matrix in DS/CDMA. A forward/backward averaging scheme decorrelates the received correlated signal after despreading in a matched filter and the Hermitian Toeplitzation scheme enhances the performance of the received signal by removing the undesired effect obtained from an array covariance matrix estimation. It is shown through simulation results that the performance of the proposed algorithm is very superior to that of the conventional Wiener maximal ratio combining (MRC) algorithm     

Adaptive noise removal from biomedical signals using warpedpolynomials

Presents the time-warped polynomial filter (TWPF), a new interval-adaptive filter for removing stationary noise from nonstationary biomedical signals. The filter fits warped polynomials to large segments of such signals. This can be interpreted as low-pass filtering with a time-varying cutoff frequency. In optimal operation, the filter's cut-off frequency equals the local signal bandwidth. However, the author also presents an iterative filter adaptation algorithm, which does not rely on the (complicated) computation of the local bandwidth. The TWPF has some important advantages over existing adaptive noise removal techniques: it reacts immediately to changes in the signal's properties, independently of the desired noise reduction; it does not require a reference signal and can be applied to nonperiodical signals. In case of quasiperiodical signals, applying the TWPF to the individual signal periods leads to an optimal noise reduction. However, the TWPF can also be applied to intervals of fixed size, at the expense of a slightly lower noise reduction. This is the way nonquasiperiodical signals are filtered. The author presents experimental results which demonstrate the usefulness of the interval-adaptive filter in several biomedical applications: noise removal from ECG, respiratory and blood pressure signals, and base-line restoration of electroencephalograms (EEGs)     

Frequency estimation of distorted non-stationary signals using complex H∞ filter

Mechanical vibration signals are always composed of harmonics of different order. A novel estimator is proposed for estimating the frequency of sinusoidal signals from measurements corrupted by White Gaussian noise with zero mean. Also low frequency sinusoidal signal is considered along with third and fifth order harmonics in presence of noise for estimating amplitudes and phases of different harmonics. The proposed estimator known as complex H_∞ filter is applied to a noisy sinusoidal signal model. State space modeling with two and three state variables is used for estimation of frequency in presence of white noise. Various comparisons in terms of simulation results for time varying frequency reveal that the proposed adaptive filter has significant improvement in noise rejection and estimation accuracy. Comparison in performance between two and three states modeling is presented in terms of mean square error (MSE) under different SNR conditions .The computer simulations clearly indicate that two states modeling based on Hilbert transform performs better than three states modeling under high noisy condition. Frequency estimation performance of the proposed filter is also being compared with extended complex Kalman filter (ECKF) under same noisy conditions through simulations.     

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。     

基于自适应EKF的相对导航算法研究

针对测距测角相对导航中测量噪声不可精确获知往往导致相对定位精度下降的问题,本文研究了基于自适应扩展卡尔曼滤波(EKF)的相对导航算法。利用泰勒级数展开对测量矩阵进行线性化处理,并利用自适应时变噪声估计方法对测量噪声方差阵进行动态估计,状态噪声方差阵通过惯导特性的先验值获得。仿真结果表明,基于自适应EKF的相对导航算法可获得高精度且连续平滑的相对定位信息,尤其在测量噪声发生变化时更是表现出良好的导航参数估计性能。     

改进的平方根容积卡尔曼滤波及其在POS中的应用

为解决扩展卡尔曼滤波在处理复杂非线性状态估计时,存在收敛速度慢、估计精度低及数值稳定性差等问题,引入一种改进的平方根容积卡尔曼滤波算法(A-SRCKF)。该算法在容积卡尔曼滤波基础上引入矩阵QR分解、Cholesky分解因数更新等技术,避免了矩阵分解、求逆及求导等复杂运算,极大降低了计算复杂度;并针对系统时变及统计特性未知情况下量测噪声协方差阵难以获取问题,通过引入自适应噪声估计器并结合小波卡尔曼滤波思想,构造出加权量测噪声协方差阵,提高了数值精度及稳定性。将A-SRCKF应用于机载定姿定位系统中,仿真结果表明:该算法有效地提升了估计精度,并且运行速度较快。     

非高斯杂波下雷达目标跟踪算法改进研究

针对杂波干扰环境中的非高斯特性,发现海杂波噪声、闪烁噪声等具有显著尖峰的非高斯噪声可以采用α稳定分布来描述,用α稳定分布可以建立更符合实际的噪声模型。根据统计信号处理最新理论和技术,利用p阶分数相关和分数低阶协方差替代传统相关和协方差来改进Kalman滤波器,优化获得改进的基于分数低阶统计量Kalman滤波交互多模型算法(Based FLOS-Kalman-IMM),仿真验证了Based FLOS-Kalman-IMM滤波跟踪新算法可以更好地适应非高斯复杂环境,得到稳健的雷达跟踪效果。     

基于改进差分阈值算法的心电检测技术研究

针对差分阈值算法中固定阈值的局限性,文中提出了一种基于自适应波峰阈值和R波间隔阈值的算法。该算法结合心电信号特点自动选择波峰阈值,并选择R波间隔阈值,提高了算法的自适应性和准确率。文中以MIT-BIH心律失常数据库中的心电信号作为实验样本,采用带通滤波与小波阈值滤波相结合的方法完成心电信号去噪,采用改进差分自适应阈值算法对心电信号进行波形检测。实验结果表明,该算法能够将心电信号R波的检测准确率提升到99.57%,有效减少了误检、漏检问题的发生,并可准确完成心率、心率变异性、身体疲劳度、精神疲劳度计算和常见心律失常分类。     

GSVD-based optimal filtering for single and multimicrophone speech enhancement

A generalized singular value decomposition (GSVD) based algorithm is proposed for enhancing multimicrophone speech signals degraded by additive colored noise. This GSVD-based multimicrophone algorithm can be considered to be an extension of the single-microphone signal subspace algorithms for enhancing noisy speech signals and amounts to a specific optimal filtering problem when the desired response signal cannot be observed. The optimal filter can be written as a function of the generalized singular vectors and singular values of a speech and noise data matrix. A number of symmetry properties are derived for the single-microphone and multimicrophone optimal filter, which are valid for the white noise case as well as for the colored noise case. In addition, the averaging step of some single-microphone signal subspace algorithms is examined, leading to the conclusion that this averaging operation is unnecessary and even suboptimal. For simple situations, where we consider localized sources and no multipath propagation, the GSVD-based optimal filtering technique exhibits the spatial directivity pattern of a beamformer. When comparing the noise reduction performance for realistic situations, simulations show that the GSVD-based optimal filtering technique has a better performance than standard fixed and adaptive beamforming techniques for all reverberation times and that it is more robust to deviations from the nominal situation, as, e.g., encountered in uncalibrated microphone arrays.     

Statistical AM-FM models, extended Kalman filter demodulation,Cramer-Rao bounds, and speech analysis

A stochastic dynamical system model for describing time signals that are jointly amplitude (AM) and frequency (FM) modulated is presented. The signal is assumed to be bandpass, perhaps originating from a filter bank applied to a broadband signal, and includes the constraint that the magnitude of the complex baseband signal is positive. Motivated by speech processing and the desire for narrowband modulating signals, time is divided into frames, and the modulating signals are smoothly interpolated across each frame. The model allows a detailed characterization of the bandwidth of the modulating signals and the statistical character of the measurement noise. An adaptive estimation algorithm based on extended Kalman filtering ideas for extracting the modulating signals from the measured signal is described and demonstrated on both voiced and unvoiced speech signals. The Cramer-Rao bound on the performance of any estimator is computed