Parallel frequency tracking with built-in performance evaluation

The problem of estimation of instantaneous frequency of a nonstationary complex sinusoid (cisoid) buried in wideband noise is considered. The proposed approach employs a bank of adaptive notch filters, extended with a nontrivial performance assessment mechanism which automatically chooses the best performing filter in the bank. Additionally, a computationally attractive method of implementing the bank is proposed. The new structure allows one to improve tracking results considerably, especially in nonstationary conditions. In terms of accuracy of frequency estimates, the proposed scheme outperforms existing ones considerably.     

一种基于自适应陷波器的科氏流量计频率解算新方法

为提高科氏流量计信号的初始收敛速度和频率跟踪精度,提出了一种频率解算的新方法:首先采用基于burg算法实现的格型IIR自适应陷波器对信号滤波,并短时间跟踪信号频率,其接近收敛时基于简化梯度算法实现的格型自适应陷波器开始并行工作,待简化梯度算法实现的格型自适应陷波器收敛后,前者停止工作,简化梯度算法实现的格型自适应陷波器持续工作直至结束。仿真及实验结果表明,本方法可以获得更快的收敛速度、更高的频率跟踪精度。     

基于Gray-Markel结构的多点自适应陷波算法研究

为了有效抑制短波通信中的窄带干扰,提出了一种改进的格型自适应多点陷波算法。该算法在Gray-Markel结构陷波器基础上,采用LMS-Newton迭代不断地更新陷波器参数。实验结果表明,算法具有比较理想的收敛性和鲁棒性。     

异构复杂信息网络下的异常数据检测算法

异构复杂信息网络承载着不同的协议和网络信道,并通过云储存实现资源调度,由此产生的异常数据会给网络信息空间带来安全威胁和存储开销,所以需要进行异常数据准确检测。传统的检测算法采用简化梯度算法进行异常数据检测,不能有效去除多个已知干扰频率成分的异常数据,检测性能不好。提出一种基于自适应陷波级联模型的异常数据检测算法。构建异构复杂信息网络系统模型,采用固有模态分解把异常数据信号解析模型分解为多个窄带信号,设计二阶格型陷波器结构,用多个固定陷波器级联抑制干扰成份,采用匹配投影法寻求优化特征解,找出所有匹配的特征点对,从而实现异常数据检测的改进。仿真实验表明,采用该算法进行异常数据检测时,信号幅值大于干扰噪声数据幅值;该算法提高了检测性能,具有较好的抗干扰性能。     

Self-optimizing generalized adaptive notch filters — comparison of three optimization strategies

The paper provides comparison of three different approaches to on-line tuning of generalized adaptive notch filters (GANFs) — the algorithms used for identification/tracking of quasi-periodically varying dynamic systems. Tuning is needed to adjust adaptation gains, which control tracking performance of GANF algorithms, to the unknown and/or time time-varying rate of system nonstationarity. Two out of three compared approaches are classical solutions — the first one incorporates sequential optimization of adaptation gains while the second one is based on the concept of parallel estimation. The main contribution of the paper is that it suggests the third way — it shows that the best results can be achieved when both approaches mentioned above are combined in a judicious way. Such joint sequential/parallel optimization preserves advantages of both treatments: adaptiveness (sequential approach) and robustness to abrupt changes (parallel approach). Additionally the paper shows how, using the concept of surrogate outputs, one can extend the proposed single-frequency algorithm to the multiple frequencies case, without falling into the complexity trap known as the “curse of dimensionality”.     

Self-tuning adaptive frequency tracker

An automatic gain tuning algorithm is proposed for a recently introduced adaptive notch filter. Theoretical analysis and simulations show that, under Gaussian random-walk type assumptions, the proposed extension is capable of adjusting adaptation gains of the filter so as to minimize the mean-squared frequency tracking error without prior knowledge of the true frequency trajectory. A simplified one degree of freedom version of the filter, recommended for practical applications, is proposed as well.     

有源电力滤波器谐波检测的新型SRF-PLL设计研究

针对有源电力滤波器中基于同步坐标系的锁相环(synchronous reference frame PLL,SRF-PLL)在三相电网电压不平衡且有畸变时,负序基频分量和高频谐波电流对SRF-PLL的影响。文章采用在基于同步坐标系的锁相环中加入自适应陷波器(adaptive notch filter,ANF)和自适应滤波器(adaptive filter)的技术。利用自适应陷波器的两个相互正交的输出量抵消同频负序分量导致的2倍工频波动,并采用自适应滤波器的最小均方算法(Least Mean Square,LMS)滤除高频谐波分量,从而准确的提取基波电压的幅值与相位。文章以单独加入自适应滤波器或自适应陷波器作比较,通过仿真分析验证文中方法能更加准确的提取电网电压频率,正余弦函数曲线更加平滑,具有良好的动态性能和稳定性。     

Cascade Prediction Filters With Adaptive Zeros to Track the Time-Varying Resonances of the Vocal Tract

In this paper, a simple and reliable technique is proposed to track vocal tract resonances in continuous speech. The approach is based on the use of predictor filters with adaptive zeros whose constrained trajectories guarantee the successful tracking of the frequency and the damping of each resonance. The zeros are adapted using a gradient-based algorithm to minimize an instantaneous prediction residual according to the principle of minimal disturbance yielding an adaptive structure capable of tracking fast-changing resonance parameters.     

一种抗遮挡与重采样的粒子滤波跟踪算法研究

针对经过多次迭代之后粒子滤波因粒子匮乏,对于光照、遮挡与旋转等问题会出现跟踪精度下降,甚至失败等问题,提出了一种似然分布自适应调整ALD方法,根据噪声因子的大小来自适应调整似然分布状态,增加先验和似然的重叠区域,有效提高滤波的稳定性,减少重采样次数;在跟踪精度不高或失败时,用局部三值模式LTP来判定所要跟踪区域,根据有效粒子所占用的面积采用动态的粒子阈值来减少重采样次数,采用模板更新来继续跟踪。实验结果表明,该算法的采样次数更少,在遮挡、旋转等条件下能有效地跟踪目标。     

基于改进粒子群算法的自适应陷波器设计

在信号处理中,接收信号常伴随着干扰和噪声,这就需要最优滤波器来实现,其中工频干扰的消除则以自适应陷波器为最优。利用粒子群算法自适应地调节其权值,得到与干扰信号接近的期望信号,最终达到消除干扰得到有用信号的目的。同时,针对此算法存在局部收敛和收敛速度不高的问题,提出了改进方法。计算机仿真结果表明了该改进粒子群算法在自适应陷波器设计上的有效性,并取得了较高的效率。     

An adaptive notch filter for suppressing mechanical resonance in high track density disk drives

In disk drive servo system, a notch filter is usually used to suppress mechanical resonance of an actuator. However, the resonance frequency differs from drive to drive due to manufacturing tolerance and varies with temperature even within a single drive. In this paper, we present an adaptive digital notch filter that can identify the resonance frequency of the actuator quickly and can adjust automatically its center frequency. For the fast identification of resonance frequency, a new estimation filter and its tuning algorithm are developed. Also, we give a rigorous analysis for the convergence of our tuning algorithm. We furthermore present some experimental results using a commercially available hard disk drive in order to demonstrate the practical use of our work.     

Enhancing the tracking capability of recursive least p-norm algorithm via adaptive gain factor

The main drawback of the recursive least p-norm (RLpN) adaptive-filtering algorithm is a poor tracking performance in the presence of abrupt changes in the model. In this paper, a new method to enhance tracking capability of the RLpN (ET-RLpN) algorithm is proposed, which uses the adaptive gain factor in the cross-correlation vector and the input-signal autocorrelation matrix to enhance tracking capability. Simulation results in system identification and echo cancellation applications are presented, which demonstrate that the ET-RLpN achieves improved tracking capability compared to the conventional RLpN and controlled adaptive combination of two RLpN filters (CAC-RLpN).     

Closed-form unbiased frequency estimation of a noisy sinusoid using notch filters

In this note, the problem of the frequency estimation of a sinusoid embedded in white noise is considered. The approach used herein is the minimization of the sample variance of the output of constrained notch filters fed by the noisy sinusoid. In particular, this note focuses on closed-form expressions of the frequency estimate, which can be obtained using notch filters having an all-zeros finite-impulse response (FIR) structure. The results presented in this note are as follows: 1) it is shown that the FIR notch filters obtained from standard second-order infinite-impulse response (IIR) filters are inadequate; 2) a new second-order IIR notch filter is proposed, which provides an unbiased estimate of the frequency; 3) the FIR filter obtained from the new IIR filter provides a closed-form unbiased frequency estimate; and 4) the closed-form frequency estimate obtained using the new FIR notch filter asymptotically converges toward the Pisarenko harmonic decomposition estimator and the Yule-Walker estimator.     

On-line order estimation and parameter identification for linear stochastic feedback control systems (CARMA model)

This paper presents a new recursive estimate method for orders and coefficients of linear stochastic feedback control systems (CARMA model) under the assumption that the upper bounds of system orders are known. The strong consistency of the estimates for orders and coefficients is proved and the convergence rate of coefficient estimates to their true values is also obtained. The estimate algorithm is applied to adaptive tracking of the systems with unknown orders and unknown coefficients. The resulting closed-loop systems are then globally stable and the tracking sample mean square error is minimized as well. Simultaneously, the estimates of the adaptive tracking for orders and coefficients are also strongly consistent. The simulation results given here show that the new developed algorithms of both system identification and adaptive tracking are effective.     

基于深度估计和特征融合的尺度自适应目标跟踪算法

针对核相关滤波目标跟踪算法(KCF)使用单特征来描述所跟踪的目标,在复杂环境下,目标尺 度发生较大变化时,无法准确跟踪目标的问题,提出基于深度估计和特征融合的尺度自适应目标跟踪算法。首 先利用深度神经网络估计视频序列中目标的深度,建立并训练深度-尺度估计模型;在跟踪过程中,融合目标 方向梯度直方图(HOG)特征和 CN (Color Name)特征训练相关滤波器,利用深度估计网络得到目标深度值,并 利用深度-尺度估计模型得到目标的尺度值,从而在目标尺度发生变化时,能够调整目标框大小,实现尺度自 适应的目标跟踪算法。实验结果表明,与经典的 KCF 算法相比,可获得更高的精度,与尺度自适应的判别型 尺度空间跟踪(DSST)算法相比,在尺度变化较大时,跟踪速度更快;在环境复杂、目标被遮挡时,鲁棒性更好。     

跟踪反舰导弹的机动频率自适应算法

为使建立的系统方程更适合强机动反舰导弹的真实运动状态,在分析"当前"统计模型及自适应滤波算法的基础上.通过由滤波残差的变化来检测目标机动与否的准则,设计了一种非线性的机动频率函数.实现机动频率自适应调整,优化了"当前"统计模型的系统参数,构建了机动频率自适应算法.在想定的初始条件下,结合反舰导弹的末段"蛇行机动",对建立的机动频率自适应算法进行Monte Carlo仿真实验,结果表明:该算法运行稳定,适应能力强,能有效提高"当前"统计模型的跟踪性能.     

自适应卡尔曼滤波在目标跟踪系统中的应用

目标跟踪是精确制导系统中的重要组成部分.文中针对运动目标跟踪问题,在建立运动模型的基础上,应用卡尔曼滤波算法进行了跟踪仿真研究.考虑到直角坐标系下的扩展卡尔曼滤波容易发散,可能导致滤波精度变差,所以文章提出一种针对非线性观测模型和线性动态模型的自适应推广卡尔曼滤波器.直角坐标系下的自适应卡尔曼滤波算法,对虚拟噪声进行了估计,动态补偿观测器模型的线性化误差,削减了系统的观测误差,并对其滤波理论及算法进行了仿真研究.结果表明:该算法提高了滤波的稳定性、快速性和精确性,优于一般的扩展卡尔曼滤波算法.     

基于基波频率估计的多谐波分析

采用自适应陷波滤波器实现基波频率可变的多谐波(包含整数次谐波和非整数次间谐波)分析. 算法包括基波频率估计器和多个二维正弦跟踪器, 形成缓慢自适应积分流形, 用李雅普诺夫定理和平均方法证明积分流形的存在性和稳定性. 若滤波器频率系数和信号的谐波结构相同, 该自适应陷波滤波器是一致渐近稳定的, 可按指数收敛准确跟随基波频率、每个谐波(间谐波)及其幅值. 导出了频率特性表达式和频率特性矩阵, 分析了滤波器参数对稳态频率特性的影响. 通过仿真证实算法的有效性, 并说明减小滤波器带宽参数和自适应增益能够获得更好的噪声特性.     

Heuristic particle filter: applying abstraction techniques to the design of visual tracking algorithms

Abstract: Many real‐world visual tracking applications have a high dimensionality, i.e. the system state is defined by a large number of variables. This kind of problem can be modelled as a dynamic optimization problem, which involves dynamic variables whose values change in time. Most applied research on optimization methods have focused on static optimization problems but these static methods often lack explicit adaptive methodologies. Heuristics are specific methods for solving problems in the absence of an algorithm for formal proof. Metaheuristics are approximate optimization methods which have been applied to more general problems with significant success. However, particle filters are Monte Carlo algorithms which solve the sequential estimation problem by approximating the theoretical distributions in the state space by simulated random measures called particles. However, particle filters lack efficient search strategies. In this paper, we propose a general framework to hybridize heuristics/metaheuristics with particle filters properly. The aim of this framework is to devise effective hybrid visual tracking algorithms naturally, guided by the use of abstraction techniques. Resulting algorithms exploit the benefits of both complementary approaches. As a particular example, a memetic algorithm particle filter is derived from the proposed hybridization framework. Finally, we show the performance of the memetic algorithm particle filter when it is applied to a multiple object tracking problem.     

Robust formant tracking for continuous speech with speaker variability

Several algorithms have been developed for tracking formant frequency trajectories of speech signals, however most of these algorithms are either not robust in real-life noise environments or are not suitable for real-time implementation. The algorithm presented in this paper obtains formant frequency estimates from voiced segments of continuous speech by using a time-varying adaptive filterbank to track individual formant frequencies. The formant tracker incorporates an adaptive voicing detector and a gender detector for formant extraction from continuous speech, for both male and female speakers. The algorithm has a low signal delay and provides smooth and accurate estimates for the first four formant frequencies at moderate and high signal-to-noise ratios. Thorough testing of the algorithm has shown that it is robust over a wide range of signal-to-noise ratios for various types of background noises.