一种新的快速自适应滤波算法的研究

从信号前后时刻相关性角度出发,提出了一种新的快速自适应滤波算法,并证明了其最优权值是一种广义的维纳方程解。该算法结构简单,收敛速度快,稳态失调小,具有处理多种信号的能力,包括非相关和强相关信号,而其计算量与NLMS算法相当。仿真结果表明,对于非相关信号,新算法的稳态失调小于VS-NLMS算法, 收敛速度与参考文献已有算法相当,但快于L.E-LMS算法;对于强相关性信号,新算法的稳态失调小于NLMS 算法和DCR-LMS算法。     

A fast recursive total least squares algorithm for adaptive IIR filtering

This work develops a new fast recursive total least squares (N-RTLS) algorithm to recursively compute the total least squares (TLS) solution for adaptive infinite-impulse-response (IIR) filtering. The new algorithm is based on the minimization of the constraint Rayleigh quotient in which the first entry of the parameter vector is fixed to the negative one. The highly computational efficiency of the proposed algorithm depends on the efficient computation of the gain vector and the adaptation of the Rayleigh quotient. Using the shift structure of the input data vectors, a fast algorithm for computing the gain vector is established, which is referred to as the fast gain vector (FGV) algorithm. The computational load of the FGV algorithm is smaller than that of the fast Kalman algorithm. Moreover, the new algorithm is numerically stable since it does not use the well-known matrix inversion lemma. The computational complexity of the new algorithm per iteration is also O(L). The global convergence of the new algorithm is studied. The performances of the relevant algorithms are compared via simulations.     

A quasi-Newton adaptive algorithm for generalized symmetriceigenvalue problem

We first recast the generalized symmetric eigenvalue problem, where the underlying matrix pencil consists of symmetric positive definite matrices, into an unconstrained minimization problem by constructing an appropriate cost function. We then extend it to the case of multiple eigen-vectors using an inflation technique. Based on this asymptotic formulation, we derive a quasi-Newton-based adaptive algorithm for estimating the required generalized eigen-vectors in the data case. The resulting algorithm is modular and parallel, and it is globally convergent with probability one. We also analyze the effect of inexact inflation on the convergence of this algorithm and that of inexact knowledge of one of the matrices (in the pencil) on the resulting eigenstructure. Simulation results demonstrate that the performance of this algorithm is almost identical to that of the rank-one updating algorithm of Karasalo (1986). Further, the performance of the proposed algorithm has been found to remain stable even over 1 million updates without suffering from any error accumulation problems     

An adaptive quasi-Newton algorithm for eigensubspace estimation

We derive and discuss a new adaptive quasi-Newton eigen-estimation algorithm and compare it with the RLS-type adaptive algorithms and the quasi-Newton algorithm proposed by Mathew et al. (1995) through experiments with stationary and nonstationary data.     

A fast frequency-domain adaptive algorithm

A time-varying convergence factor μ is proposed for the frequency-domain LMS (least-mean-square) adaptive algorithm, which results in the frequency-domain optimal block algorithm (FOBA). The FOBA is the frequency-domain implementation of the recently proposed optimum block algorithm (OBA). The FOBA results in computational savings in comparison to the OBA and in performance enhancement relative to the frequency-domain LMS algorithm     

Multichannel linear and quadratic adaptive filtering based on theChandrasekhar fast algorithm

A new fast algorithm for multichannel linear and quadratic adaptive filtering using the Chandrasekhar equations is presented. Based on the shift-invariance property, the multichannel linear model could be described by a time-invariant state-space model to which we apply the Chandrasekhar factorization technique, which provides interesting numerical properties. Furthermore, a new method for nonlinear filtering is given where the multichannel Chandrasekhar algorithm is applied on the second-order Volterra (SOV) filter after suitable transformations     

A fast algorithm for general Volterra filtering

Volterra filters are a classical instrument for nonlinear channels and systems modeling, noise and echo cancellation, signal estimation and detection, and various other applications. As is well known, the computational weight of Volterra filters exponentially grows with the nonlinearity degree. This work presents a contribution to the efficient computation of Volterra filters with generic order nonlinearity found in many telecommunication applications. Our technique rests on the interpretation of the Mth-order one-dimensional Volterra filters in terms of M-dimensional linear convolution, and it adopts a multidimensional fast convolution scheme. This makes the method applicable to any M. Interestingly enough, fast convolution based on the standard multidimensional fast Fourier transform (MD FFT) in the case of Volterra filters is outperformed by direct computation. Our method is efficient due to the use of a special MD FFT which can exploit the symmetries of the signals entering the computation of Volterra filters and which makes it superior to direct computation. The points of interests of the results presented are both the generality and the fact that they show that the well-known nonlinearity/multidimensionality tradeoff of Volterra filters can have computational implications.     

A fast filtering algorithm for image enhancement

A filtering algorithm for fast image enhancement is described. The algorithm tries to make the minimum modification on the original image structures while it performs noise smoothing at a given filtering level. The filtered image is a weighted combination of four subimages obtained from low-pass filtering the original image along four major directions. The weighting on each subimage is controlled by the differences between these subimages and the original image The resulting image is then nonsymmetrically sharpened to enhance the image structure boundaries, The overall effect of this filtering structure is effective adaptive noise reduction and edge enhancement with an efficient implementation using array processors. The high regularity and parallelism of the algorithm also makes it suitable for its efficient implementation using very large scale integrated (VLSI) circuits or multiprocessor systems. The performance of the algorithm in effectively reducing image noise and preserving/enhancing important image structures is discussed and demonstrated using several MR images from a low-field-strength MR imaging system.     

A new fast block adaptive algorithm

This paper describes a novel efficient algorithm appropriate for adapting filters of long order. The scheme is an exact block processing counterpart of the fast Newton transversal filtering algorithm. The filters required by the algorithm blocks are much smaller than the filter length, and the obtained estimates are mathematically equivalent to those of the sample-by-sample version. This leads to a substantial saving in computational complexity without sacrificing performance as well as not having to resort to long processing delays, which limit the performance of the adaptive system     

A fast exact FTF adaptive algorithm

In this paper we present a new method for the efficient implementation of the fast transversal filter(ftf) algorithm. Reduction of the arithmetic complexity is obtained by making use of the redundancy in the successive computations of the forward prediction error and the filtering error in the joint process. The resulting algorithm is exactly equivalent to the originalftf algorithm, hence retaining the same theoretical convergence characteristics and offering the least squares(ls) estimate at each recursion step without delay. Furthermore, the algorithm can be numerically stabilized by using a simple and effective stabilization measure which needs only one additional multiplication per recursion step. The equivalence of the proposed algorithm to the originalftf algorithm is demonstrated by simulations of an acoustic room impulse response identification.     

一种分阶自适应Volterra滤波算法

稳定分布可以更好地描述实际应用中所遇到的具有显著脉冲特性的随机噪声.这种噪声的二阶及二阶以上统计量均不存在,需要用分数低阶统计量描述.针对Volterra级数非线性项将稳定分布的尖峰脉冲特性更加放大,导致输入信号自相关矩阵的特征值扩展更大的问题,本文提出了一种对于二阶Volterra级数的线性项部分和非线性部分分别采用两个不同收敛因子的分阶Volterra滤波器最小平均p范数(DOVLMP)算法,并分析证明了该算法的收敛性能.仿真结果验证了本文方法较传统算法的优越性.     

A HOMOTOPY CONTINUATION ADAPTIVE IIR FILTERING ALGORITHM

A homotopy continuation adaptive HR filtering algorithm is proposed in this paper.The novel algorithm introduces the homotopy continuation method into the adaptive filtering soas to provide a high stability for adaptive HR filter without any forms of stability monitoringattached.     

A numerically stable fast RLS algorithm for adaptive filtering andprediction based on the UD factorization

The use of UD factorization in adaptive RLS algorithms is interesting for its numeric robustness and because no square-root operations at all are involved. We describe a square root free fast RLS algorithm based on the UD factorization of the autocorrelation matrix. Numerous finite precision simulations tend to indicate that this algorithm is numerically stable. The algorithm requires 𝒪(𝒩) operations, where 𝒩 is the linear filter order     

基于拟牛顿算法的复自适应均衡器的设计

介绍了一种新的拟牛顿算法，这种算法的收敛速度与RLS算法相当，但稳定性要比RLS算法好，误码率要比LMS算法低，详述了基于该算法的复自适应均衡器的软硬件设计与实现。     

一种基于三阶累积量的准则及自适应滤波算法

该文提出了一个基于三阶累积量的优度准则。基于此准则,利用最速下降法,得到一种新的基于三阶累积的梯度型自适应滤波算法,该算法用于平稳和非平稳的MA(Moving Average)模型系统辨识的计算机模拟仿真结果表明:该算法有良好的收敛性能及对时变系统的跟踪能力。     

A simple fast adaptive zero tracking algorithm

This paper presents and investigates a simple and fast zero tracking algorithm for adaptive array processing. With this algorithm, the complex zeroes of the array directional pattern are repetitively updated one at a time by using the LMS algorithm to minimize the output power. Since only one zero is adjusted at any instant, the complexity required to implement the new algorithm is almost the same as that of using the LMS algorithm directly. However, unlike the latter algorithm, the convergence behaviour of the new algorithm is significantly faster and is almost independent of the external noise environment.     

A recursive least M-estimate algorithm for robust adaptive filtering in impulsive noise: fast algorithm and convergence performance analysis

This paper studies the problem of robust adaptive filtering in impulsive noise environment using a recursive least M-estimate algorithm (RLM). The RLM algorithm minimizes a robust M-estimator-based cost function instead of the conventional mean square error function (MSE). Previous work has showed that the RLM algorithm offers improved robustness to impulses over conventional recursive least squares (RLS) algorithm. In this paper, the mean and mean square convergence behaviors of the RLM algorithm under the contaminated Gaussian impulsive noise model is analyzed. A lattice structure-based fast RLM algorithm, called the Huber Prior Error Feedback-Least Squares Lattice (H-PEF-LSL) algorithm is derived. Part of the H-PEF-LSL algorithm was presented in ICASSP 2001. It has an order O(N) arithmetic complexity, where N is the length of the adaptive filter, and can be viewed as a fast implementation of the RLM algorithm based on the modified Huber M-estimate function and the conventional PEF-LSL adaptive filtering algorithm. Simulation results show that the transversal RLM and the H-PEF-LSL algorithms have better performance than the conventional RLS and other RLS-like robust adaptive algorithms tested when the desired and input signals are corrupted by impulsive noise. Furthermore, the theoretical and simulation results on the convergence behaviors agree very well with each other.     

A direction set based algorithm for adaptive filtering

A new algorithm is developed for adaptive filtering applications. This algorithm is based on a direction set method and has a computational complexity of O(N) for each update of the system. The method exploits the structure of the objective function and maintains a set of near-conjugate directions with respect to the Hessian. This algorithm has a rapid rate of convergence that is comparable with that of the well-known RLS method. The performance of the algorithm is illustrated with adaptive filtering applications     

A fast algorithm for 1-norm vector median filtering

A major drawback with vector median filters is their high computational complexity. A fast algorithm is presented for the computation of the vector median operator based on 1-norm. The algorithm complexity is investigated both from a theoretical and an experimental point of view. Simulation results are shown proving the complexity reduction achieved by the novel algorithm.