基于ANFIS的非线性自适应噪声消除

本文给出了ANFIS的一般描述,阐述了使用ANFIS进行噪声消除的原理,并给出了针对含噪语音的仿真实验结果。     

Nonlinear adaptive bilinear filters for active noise control systems

The reference and error channels of active noise control (ANC) systems may be saturated in real-world applications if the noise level exceeds the dynamic range of the electronic devices. This nonlinear saturation degrades the performance of ANC systems that use linear adaptive filters with the filtered-X least-mean-square (FXLMS) algorithm. This paper derives a bilinear FXLMS algorithm for nonlinear adaptive filters to solve the problems of signal saturation and other nonlinear distortions that occur in ANC systems used for practical applications. The performance of this bilinear adaptive filter is evaluated in terms of convergence speed, residual noise in steady state, and the computational complexity for different filter lengths. Computer simulations verify that the nonlinear adaptive filter with the associated bilinear FXLMS algorithm is more effective in reducing saturation effects in ANC systems than a linear filter and a nonlinear Volterra filter with the FXLMS algorithm.     

Multistage adaptive compensation of active noise interferences using block orthogonalization of signals of compensation channels

The multistage digital automatic compensators of active noise interferences have been synthesized using the block Gram-Schmidt orthogonalization and LS-algorithm based on the least squares criterion. The specified automatic compensators ensure parallel-serial processing of signals. The application of RLS-algorithm made it possible to obtain a recurrent procedure for calculation of weighting coefficients of automatic compensator modules represented in the form of multiinput weighting adders. Statistical computer simulation was used to analyze the multistage digital automatic compensators based on the RLS-algorithm with simultaneous adaptation of weighting adders of all stages. As a result of parallelization of computational process, the synthesized multistage automatic compensators made it possible to increase the speed of processing (signal sampling frequency) by one order of magnitude and more as compared to that of automatic compensators built on single-stage scheme.     

Nonlinear noise cancellation for image with adaptive neuro-fuzzy inference systems

The adaptive neural-fuzzy inference system (ANFIS) algorithm is proposed for nonlinear noise cancellation of images. The quality in terms of mean square error of image restoration using the proposed ANFIS is over 65 times better for Gaussian noise and 500 times better for salt and pepper noise than using conventional filtering systems.     

On adaptive beamforming in correlated noise

This paper deals with the problem of adaptive beamforming in the presence of fully coherent (correlated) noise sources. Two different techniques are developed for minimizing the effects of coherent interference. The first method employs spatial interpolation of the array aperture, followed by spatial smoothing in order to decorrelate the desired signal and the interference. The second technique is based on a simple algebraic transformation for restoring the rank of the array signal correlation matrix, which is normally rank deficient in such situations. This technique is shown to work for nonuniform adaptive arrays as well. Extensive computer simulation results are presented to illustrate the effectiveness of the proposed techniques.This week was supported by the N.R.C., Resident Research Associateship Programme.     

Nonlinear effects in LMS adaptive equalizers

An adaptive transversal equalizer based on the least-mean-square (LMS) algorithm, operating in an environment with a temporally correlated interference, can exhibit better steady-state mean-square-error (MSE) performance than the corresponding Wiener filter. This phenomenon is a result of the nonlinear nature of the LMS algorithm and is obscured by traditional analysis approaches that utilize the independence assumption (current filter weight vector assumed to be statistically independent of the current data vector). To analyze this equalizer problem, we use a transfer function approach to develop approximate analytical expressions of the LMS MSE for sinusoidal and autoregressive interference processes. We demonstrate that the degree to which LMS may outperform the corresponding Wiener filter is dependent on system parameters such as signal-to-noise ratio (SNR), signal-to-interference ratio (SIR), equalizer length, and the step-size parameter     

Frame-synchronous noise compensation for hands-free speechrecognition in car environments

It has become increasingly important to develop hands-free speech recognition techniques for the human-computer interface in car environments. However, severe car noise degrades the speech recognition performance substantially. To compensate the performance loss, it is necessary to adapt the original speech hidden Markov models (HMMs) to meet changing car environments. A novel frame-synchronous adaptation mechanism for in-car speech recognition is presented. This mechanism is intended to perform unsupervised model adaptation efficiently on a frame-by-frame basis instead of a conventional adaptation algorithm relying on batch adaptation data and supervision information. The proposed adaptation scheme is performed during frame likelihood calculation where an optimal equalisation factor is first computed to equalise the model mean vector and the input frame vector. This equalisation factor then serves as a reference index to retrieve an additional bias vector for model mean adaptation. As a result, a rapid and flexible algorithm is exploited to establish a new robust likelihood measure. In experiments on hands-free in-car speech recognition with the microphone far from the talker, this framework is found to be effective in terms of recognition rate and computational cost under various driving speeds     

Estimation and compensation of clipping noise in OFDMA systems

We propose an efficient and low-complexity scheme for estimating and compensating clipping noise in OFDMA systems. Conventional clipping noise estimation schemes, which need all demodulated data symbols, may become infeasible in OFDMA systems where a specific user may only know his own modulation scheme. The proposed scheme first uses equalized output to identify a limited number of candidate clips, and then exploits the information on known subcarriers to reconstruct clipped signal. Simulation results show that the proposed scheme can significantly improve the system performance.     

Offset compensation in comparators with minimum input-referred supply noise

The resolution of a comparator is determined by the dc input offset and the ac noise. For mixed-mode applications with significant digital switching, input-referred supply noise can be a significant source of error. This paper proposes an offset compensation technique that can simultaneously minimize input-referred supply noise. Demonstrated with digital offset compensation, this scheme reduces input-referred supply noise to a small fraction (13%) of one least significant bit (LSB) digital offset. In addition, the same analysis can be applied to analog offset compensation.     

Robust adaptive estimator for filtering noise in images

Provides three new methods for storing images corrupted by additive noise. One is the adaptive mean median filter for preserving the details of images when restored from additive Gaussian noise. Another is the minimum-maximum method for moving outlier noise. The third method, the robust adaptive mean p-median filter, is based on a combination of the previous two methods. In the past, proposed restoration methods have generally proven to be inadequate for both detail preservation and noise suppression, but the new adaptive mean p-median filter is shown to be good at both of these tasks, while the robust adaptive mean p-median filter can give good performance even in the presence of outliers. Degraded images are processed by the proposed algorithms, with the results compared with a selection of other median-based algorithms that have been proposed in the literature.     

红外零位走动量测量中的相机姿态自适应补偿

为提高红外零位走动量测量精度,针对测量过程中相机姿态变化误差引起的图像定位精度不高问题,提出了一种CCD相机姿态小角度变化自适应补偿方法。通过理论分析,推导出相机姿态解算公式,建立了CCD相机姿态解算数学模型,然后基于某型红外瞄具零位走动量测量系统,分别针对三种相机姿态,对瞄具分划线在参考坐标系中的坐标进行了对比实验。结果表明,测量精度优于0.01 mil,能减小相机倾斜对红外瞄具零位走动量测量带来的误差,为提高零位走动量测量精度提供了一种相机姿态自适应补偿的新方法。     

Digital compensation in IQ modulators using adaptive FIR filters

This paper discusses digital compensation for frequency-dependent transfer characteristics and implementation errors in digital PAM/continuous-phase frequency-shift keying (CPFSK) quadrature modulators. Recently, several methods have been proposed to digitally compensate for the shortcomings of the analog reconstruction filters in IQ modulators. While these methods have shown to be effective, they result in filters with long coefficients that are computationally demanding to implement on the DSP. Furthermore, the modulator needs to be taken offline while the precompensation filters are updated to reflect the changes in the I and Q channel characteristics. In this paper, a digital compensation method is proposed here using two adaptive finite-impulse response filters to compensate for the magnitude and phase characteristics of the analog reconstruction filters in the IQ modulator. The experimental results show that this technique is effective and lead to substantial improvement of the output envelope ripples.     

基于自适应Volterra的高斯噪声图像滤波算法

针对高斯噪声图像滤波问题,提出一种可以保留细节的自适应Volterra-Volterra滤波方法。为了解决二阶Volterra滤波器核系数确定难、数量多等问题,提出了一种自适应建核结构及算法。通过图像处理实验,与其它滤波算法进行了细节度保留、均方差(MSE)和信噪比(SNR)等指标的比较,结果表明,本文方法优于进行对比的其它滤波方法。     

Improved parameter estimation by noise compensation in the time-scale domain

It was shown recently that parameter estimation can be performed directly in the time-scale domain by isolating regions wherein the prediction error can be attributed to the error of individual dynamic model parameters [1]. Based on these single-parameter equations of the prediction error, individual model parameters error can be estimated for iterative parameter estimation. An added benefit of this parameter estimation method, besides its unique convergence characteristics, is the added capacity for direct noise compensation in the time-scale domain. This paper explores this benefit by introducing a noise compensation method that estimates the distortion by noise of the prediction error in the time-scale domain and incorporates that as a confidence factor to bias the estimation of individual parameters error. This method is shown to improve the precision of the estimated parameters when the confidence factors accurately represent the noise distortion of the prediction error.     

一种快速自适应算法在噪声抵消中的应用

研究了自适应横向滤波器的一种快速算法（FTF）及其在噪声抵消中的作用，对算法进行了推导，归纳了计算步骤，噪声抵消实验表明有良好的效果。     

脉冲噪声环境中自适应阵列对信号的跟踪

以alpha稳定分布作为无线信道的噪声模型,基于分数低阶统计量理论采用子空间和近似最大似然的方法,利用整个时隙内的接收信号恢复出期望用户的发射字符。相对于二阶统计量的方法,该方法有更好的韧性和经济的计算量。     

偏振模色散补偿系统中自适应反馈控制技术的研究

偏振模色散（PMD）已成为发展下一代高速长距离光纤传输系统的主要制约因素，本文中分析了分析了PMD补偿系统中几种自适应反馈控制技术，并详细比较，讨论了各自的特点，认为偏振度测量法是一种首选技术。     

Digital adaptive phase noise reduction in coherent optical links

Coherent optical links enable high-density constellations and, consequently, a higher throughput. However, the phase noise associated with the transmitter and the receiver lasers is a challenging issue in coherent lightwave systems. The authors present an approach that relies on using digital signal processing techniques to compensate for the laser phase-noise effects. The proposed approach exploits the digital processing power to address the problems arising from optical imperfections. The authors present an adaptive filtering scheme that reduces the effect of the laser phase noise and, consequently, relaxes the laser linewidth requirement. The proposed approach shows how the signal processing techniques can be exploited to compensate for the optical impairments by utilizing the continuing scale down in size and power in very large scale integration (VLSI) technology.     

Efficient MAP-based estimation and compensation of phase noise in MIMO-OFDM receivers

This article proposes an iterative orthogonal frequency division multiplexing receiver employing low-complexity phase noise mitigation to alleviate degradation in the channel estimation and the data detection stages of a spatially multiplexed multiple-input multiple-output communication system. Severe oscillator phase noise gives rise to inter-carrier interference that becomes a limiting factor in sustaining reliable communication link. To mitigate phase distortion in a multi-path Rayleigh fading channel, we built an iterative Bayesian algorithm based on the statistical modeling of the phase noise process. However, the resultant informed estimators require computationally intensive inversion of large dimensional matrices. Taking advantage of the approximate sparsity pattern inherent to the phase noise spectral composition and exploiting decoupled compensators, the proposed sub-optimal scheme suppresses phase noise induced perturbation with a modest complexity increment. Our claims are supported by the numerical analysis, which demonstrate the estimator efficacy considering a generalized oscillator signal distribution network based on the spatial correlation of the phase noise processes.