抗交串自适应噪声对消系统的算法实现

针对一般的自适应噪声对消系统性能上存在的不足,引入了抗交串自适应噪声对消系统.抗交串自适应噪声对消系统在形式上由两个典型的自适应噪声对消模块串接而成.抗交串自适应噪声对消系统无须预先知道信号和噪声的特征,却能够相当好地抑制噪声的影响,又使有用信号不产生畸变.该文叙述了该系统的算法实现.计算机仿真结果表明,在强噪声环境背景下,该算法将功率信噪比提高了50db左右,而且运算速度快.该算法对某些特定采样信号的分析和处理有重要意义.     

基于仿射投影算法的时延估计

在时间延迟估计中,通常利用互相关算法对时延进行估计.然而,互相关算法受噪声影响较大,在低信噪比时无法准确对时延进行估计.LMS、NLMS等自适应算法能够避免噪声的影响,然而其收敛性较差.RLS自适应算法虽具有较好的收敛性,但算法复杂度较高.本文利用的仿射投影(AP)算法则具有较好的收敛性.模拟仿真表明该算法能准确估计时...     

基于自适应卡尔曼滤波的盲多用户检测算法

对强多址干扰情况下码分多址系统的盲多用户检测算法进行研究,针对多径信道的码分多址系统,提出一种基于自适应卡尔曼滤波的盲多用户检测算法。该算法可在进行状态滤波的同时对未知噪声的统计特性进行在线估计,确保算法收敛于期望用户,提高检测器在动态环境下的跟踪性能。仿真结果表明,与最小均方算法及递推最小二乘算法相比,该算法具有更好的收敛性和动态性能。     

噪声ICA模型去偏技术研究

偏差去除算法通常假设高斯噪声条件下对普通ICA算法进行修正来消除噪声带来的影响。但是存在高斯噪声条件时,普通ICA算法对解混矩阵仍然可以辨识。故引入基于QR分解的RLS自适应噪声抵消算法和Fast-ICA算法相结合,只需对观测信号进行去噪处理,不需要对解混矩阵修正。并分别在同一噪声和相关噪声条件下做了仿真实验,与LMS-ICA算法进行了比较。仿真实验证明,该方法比后者效果显著。提出了用最小二乘算法计算分离信号的输出信噪比,作为评价算法的性能指标。     

A Machine Learning Approach for Artifact Removal from Brain Signal

Electroencephalography (EEG), helps to analyze the neuronal activity of a human brain in the form of electrical signals with high temporal resolution in the millisecond range. To extract clean clinical information from EEG signals, it is essential to remove unwanted artifacts that are due to different causes including at the time of acquisition. In this piece of work, the authors considered the EEG signal contaminated with Electrocardiogram (ECG) artifacts that occurs mostly in cardiac patients. The clean EEG is taken from the openly available Mendeley database whereas the ECG signal is collected from the Physionet database to create artifacts in the EEG signal and verify the proposed algorithm. Being the artifactual signal is non-linear and non-stationary the Random Vector Functional Link Network (RVFLN) model is used in this case. The Machine Learning approach has taken a leading role in every field of current research and RVFLN is one of them. For the proof of adaptive nature, the model is designed with EEG as a reference and artifactual EEG as input. The peaks of ECG signals are evaluated for artifact estimation as the amplitude is higher than the EEG signal. To vary the weight and reduce the error, an exponentially weighted Recursive Least Square(RLS) algorithm is used to design the adaptive filter with the novel RVFLN model. The random vectors are considered in this model with a radial basis function to satisfy the required signal experimentation. It is found that the result is excellent in terms of Mean Square Error (MSE), Normalized Mean Square Error (NMSE), Relative Error (RE), Gain in Signal to Artifact Ratio (GSAR), Signal Noise Ratio(SNR), Information Quantity (IQ), and Improvement in Normalized Power Spectrum (INPS). Also, the proposed method is compared with the earlier methods to show its efficacy.     

An efficient soft-computing technique for extraction of EEG signal from tainted EEG signal

Electroencephalography (EEG) is the recording of electrical activity of neurons within the brain and is used for the evaluation of brain disorders. But, EEG signals are contaminated with various artifacts which make interpretation of EEGs clinically difficult. In this research paper, we use a soft-computing technique called ANFIS (Adaptive Neuro-Fuzzy Inference System) for the removal of EOG artifact, combined EOG and EMG artifact. Improvement in the output signal to noise ratio and minimum mean square error are used as the performance measures. The outputs of the proposed technique are compared with the outputs of techniques such as neural network, based on ADALINE (Adaptive Linear Neuron) and adaptive filtering method, which makes use of RLS (Recursive Least Squares) algorithm through wavelet transform (RLS-Wavelet). The obtained results show that the proposed method could significantly detect and suppress the artifacts.     

基于LabVIEW的自适应滤波器设计与研究

针对自适应滤波器编程复杂,难以按照虚拟仪器系统的形式来测试工程应用中的实际性能等问题。文中利用LabVIEW8.6提供的自适应滤波器工具包,设计了基于最小均方误差算法、递推最小二乘算法的自适应滤波器,并对影响两种算法的参数对滤波器的敏感性进行了分析;进而,利用音频信号验证了滤波器性能。仿真结果表明,所设计的自适应滤波器功能全面,人机交互界面良好,便于工程技术人员快速开发,具有较好的工程实用价值。     

比特交织编码调制系统中的均衡技术仿真研究

判决反馈迭代译码的比特交织编码调制(BICM-ID)技术是一种高性能、低复杂度的先进信道调制方案.分析了BICM-ID系统的结构和迭代译码方法,研究了最小均方(LMS)和最小二乘(RLS)自适应均衡算法以及线性(LE)和判决反馈(DFE)均衡器.设计了在ISI信道下BICM-ID使用DFE-RLS均衡器的系统,计算机仿真证明其能够自适应地快速估计、跟踪信道,对于码间干扰严重的信道有良好的均衡效果,经过迭代译码有良好的误码性能-分别在ISI干扰轻微和严重的W2.9和W3.5信道下,经过3次迭代译码,在信噪比分别为7.6 dB和12.8 dB时误码率可达10-4.     

改进的变步长最小均方误差电子耳蜗语音增强算法

针对外部强噪声环境下电子耳蜗语音质量受损、适应性差等问题,提出了基于谱减法和变步长最小均方误差（LMS）自适应滤波算法联合去噪的改进方法,并以该方法构建了一个电子耳蜗前端语音预处理系统。利用变步长LMS自适应滤波算法输出误差的平方项来调节步长,采用步长值固定与变化相结合的方法,解决了自适应滤波算法收敛速度慢、稳态误差大的问题,适应性得到提高,提高了语音信号通信质量。该系统以TMS320VC5416和音频编解码芯片TLV320AIC23B为核心,通过多通道缓冲串口（McBSP）和串行外设接口（SPI）实现了语音数据的高速采集和实时处理。实验仿真和测试结果表明该算法消除噪声性能好,信噪比在低输入信噪比情况下提高约10 dB,语音质量感知评价（PESQ）分值也得到较大提高,能有效提高语音信号质量,且该系统性能稳定,能进一步提高耳蜗前端语音的清晰度和可懂度。     

A novel implementation of combined systolic and folded architectures for adaptive filters in FPGA

The adaptive filter constitutes an important part of statistical signal processing. Adaptive filters are often realized either as a set of program instructions running on an arithmetical processing device such as a Microprocessor or Digital Signal Processing (DSP) chip, or as a set of logic operations implemented in a field programmable gate array (FPGA) or in Very Large Scale Integrated Circuit (VLSI). Systolic architecture improves the speed of the system at the cost of increased area. On the other hand, folding technique uses less hardware resources. A combination of systolic and folding structures provides improvement in speed and reduction in area. This paper presents a novel idea of combining Systolic and Folding architectures and its design in various adaptive filters like Recursive Least Square (RLS), Affine Projection (AP) and Kalman filters. The structures are designed using Xilinx System Generator tool of MATlab 2015 and implemented in Xilinx Virtex 5 FPGA. The designed structures are tested for noise cancellation in Electrocardiogram (ECG) signal and results are analysed for various order of all the filters and its metrics are analysed in terms of Signal to Noise Ratio(SNR), Mean Square Error(MSE), area and speed. From the analysis it is observed that the proposed folding in systolic structures improves SNR by 6.77% in RLS, 4.68% in Affine projection and 2.13% in Kalman Algorithm than the conventional structures. It is inferred that the proposed design in Affine projection shows improved SNR than the other filters. The proposed combined folding in systolic architecture shows 18.35% reduction in area and reduction in delay.     

一种MEMS水听器信号去噪的组合算法研究

针对MEMS水听器采集的数据"淹没"在强噪声场中的问题,提出采用LMS自适应噪声对消与Fourier变换滤波相结合的组合算法实现MEMS水听器的信噪分离。在信号频率已知的情况下,设计了一种自适应噪声对消和Fourier变换滤波组合算法的滤波器,对提取后的信号与理想信号做性能对比。仿真实验表明:该组合算法在-15 dB的强噪声场中仍有较高的分辨精度和提取效果,对搜寻类似于"黑匣子"等情况比较适宜,并将设计的滤波器用于中北汾机测试实验的信噪分离中,结果验证了该算法具有良好的高效性和实用性。     

Improved performance of impulse active noise control using active function threshold with absolute harmonic variable step-size algorithm

The Standard (conventional) adaptive algorithms exhibits low convergence rate and minimum noise suppression, or else the system becomes unstable under Gaussian and non-Gaussian (impulsive noise SαS distributions) noise environments. In order to overcome the drawback of traditional algorithms (i.e., to eliminate unwanted noise), the popular algorithm Filtered Cross Minimum Square (FxLMS) is used in Active Noise Control (ANC), not only to improve its efficiency but also to improve its performance. In this paper, we proposed two improvements: first, we proposed a novel method Active threshold function FxLMS (ATFxLMS) being employed in ANC in the paths of primary (reference) and error signals; a second proposal is employing the Variable Step-Size based on Absolute Harmonic Mean (AHMVSS) of error signal. The idea behind this method is that the step-size of the algorithm varies depending on the harmonic mean of error signal obtained from the error location. In comparison to the fixed step-size algorithm, the proposed ATF-AHMVSS provided an improved convergence rate for the desired ANC efficiency. Moreover computational complication of the proposed method was examined as it was found that the proposed algorithm provided stable condition for ANC systems. Computer simulation results are revealed that the proposed (AT & AHMVSS-FxLMS) algorithm have attained excellent performance in terms of convergence speed, noise reduction and minimum steady state error as compared to other existing algorithms under different noise inputs. The results obtained from the proposed algorithm show outperformance compared to traditional adaptive algorithms.     

修正指数遗忘RLS算法及其在故障诊断上的应用

汽车电子系统在线故障诊断的算法执行效率、动态跟踪速度和稳态估计精度是检测突变故障参数档估计故障诊断中常用的递推最小二乘RLS算法存在的典型问题,提出了一种在线修正遗忘因子的方法.理论分析和仿真结果均表明,修正后的方法能有效解决一般递推算法的"数据饱和"问题,与通常的遗忘算法和滑动数据窗法相比,表现出了明显的优越性.为进一步的实车故障诊断提供了更加有效的理论根据.     

基于小波自适应算法的传感器信号降噪研究

基于MALLAT算法原理和自适应算法,设计了小波自适应算法的结构,并对算法进行了理论分析和仿真研究。仿真结果表明,小波自适应算法在传感器信号降噪方面表现出了良好的性能。     

一种基于小波理论的LMS算法研究

基于LMS算法原理和MALLAT算法,提出了小波自适应算法,并对算法进行了理论分析和仿真研究,仿真结果表明,小波自适应算法在非线性系统辩识中表现出了良好的性能。     

网络化控制系统两种时延预测算法及其比较

针对网络化控制系统（NCS）中的随机时变时延,提出了两种时延预测算法——自适应最小均方差（LMS）算法和在线最小二乘支持向量机（LS-SVM）算法,对其进行预测,并用实际测试得到的网络时延数据,对两种算法的时延预测效果进行了详细分析比较,指出了各自的特点和适用范围。     

Modified Recursive Least Squares algorithm to train the Hybrid Multilayered Perceptron (HMLP) network

In this paper, a new learning algorithm, called the Modified Recursive Least Square (MRLS), is introduced for the Hybrid Multilayered Perceptron (HMLP) network. Adopting the Recursive Least Square (RLS) algorithm as its basis, the MRLS algorithm differs from RLS in the way that the weight of the linear connections for the HMLP network is estimated. The convergence rate of the MRLS algorithm is further improved by varying the forgetting factor, optimizing the way the momentum and learning rate are assigned. To investigate its applicability, the MRLS algorithm is demonstrated on the HMLP network using six benchmark data sets obtained from the UCI repository. The classification performance of the HMLP network trained with the MRLS algorithm is compared with those of the HMLP network trained with the Modified Recursive Prediction Error (MRPE) algorithm and the MLP trained with the standard RLS algorithm as well as with other commonly adopted machine learning classifiers. The comparison results indicated that the proposed MRLS trained HMLP network provides significant improvement over RLS trained MLP network, MRPE trained HMLP network, and other machine learning classifiers in terms of accuracy, convergence rate and mean square error (MSE).     

Nonlinear Thf‐Fxlms Algorithm For Active Noise Control With Loudspeaker Nonlinearity

Adaptive algorithms are prevalently applied in the design of nonlinear active noise control (ANC) systems. The most important nonlinearity in ANC is the saturation effect produced by the electro‐acoustical sensors and transducers. The dominant saturation nonlinearity is in the transducers, which can be represented by a Wiener model. An effective solution to mitigate such nonlinear distortion is to employ the Nonlinear Filtered‐X Least Mean Square (NLFXLMS) algorithm. The controller compensates the nonlinearity using a model of the saturation effect represented by the scaled error function (SEF). However, the NLFXLMS is limited by two practical issues such that the degree of nonlinearity has to be known in advance and the SEF cannot be evaluated in real time. In this work, the NLFXLMS algorithm is modified by incorporating the tangential hyperbolic function (THF) to model the saturation effect of the loudspeaker. The proposed THF‐NLFXLMS algorithm, models the nonlinear secondary path and applies the estimated degree of nonlinearity in the control algorithm design. The results show that the Wiener secondary path, with saturation nonlinearity represented by SEF, can be modelled by THF with a certain degree of accuracy and can yield a reasonable estimate of the degree of nonlinearity. The performance of the proposed algorithm is comparable with the benchmark NLFXLMS and is superior to the conventional FXLMS as well as the second order Volterra algorithm of similar computational complexity with the proposed algorithm.     

基于微分麦克风阵列的自适应语音增强算法研究及DSP实现

自适应滤波是语音增强算法中的常用技术, 而算法复杂度与收敛速度是设计各种自适应算法需要首要考虑的问题. 本文提出一种用于片上的语音增强自适应滤波新算法. 该算法分两步实现, 首先, 利用一阶微分麦克风阵列, 获得噪声的实时估计; 其次, 对传统的仿射投影算法(Affine projection algorithm, APA)加以改进, 得到计算误差向量的快速算法, 并根据估计误差动态调整搜索步长以及仿射投影维数, 对带噪语音进行自适应滤波消噪. 在TMS320VC5509 DSP芯片上实现该算法. 实验表明, 算法的自适应滤波过程具有接近递推最小二乘算法(Recursive least squares, RLS)的快速收敛速度, 以及类似最小均方误差算法(Least mean squares, LMS)的低算法复杂度.     

改进的最小均方自适应滤波算法

针对传统的固定步长最小均方(LMS)算法应用于雷达杂波自适应滤波器系统存在收敛速度与收敛精确度相矛盾的问题,提出一种新的变步长LMS自适应滤波算法。在其基础步长迭代公式中,通过组合自相关误差与前一步长因子来实时更新迭代下一步长因子的方法,达到具有较快的收敛速度和较小的失调,并且不受已经存在的不相关噪声的干扰的效果。仿真结果表明,所提方法的实验效果与传统固定步长LMS算法及已有算法相比,在收敛速率、收敛精度、抑制噪声方面都有很大的改善,证明所提算法是有效、可行的,且与理论分析一致。