Optimal Farrow coefficients for symbol timing recovery

The Farrow structure provides an efficient way to implement interpolation filters. Such filters are often required in digital modems to allow symbol timing recovery. A method for generating Farrow filter coefficients that minimize the mean square error (MSE) at the symbol decision instants is presented. Minimizing the MSE at the symbol decision instants is nearly equivalent to minimizing the symbol error rate (SER) but is more mathematically convenient. The MSE at times other than the symbol decision instants does not affect the SER and is not considered in the optimization. Example coefficients are tabulated and performance is illustrated using SER results generated by computer simulation     

Analysis of adaptive filters using normalized signed regressor LMSalgorithm

In this paper, adaptive filters using the normalized signed regressor LMS algorithm (NSRA) with Gaussian reference inputs are proposed and analyzed to yield difference equations for theoretically calculating expected convergence of the filters. A simple difference equation for mean squared error (MSE) is derived when the filter input is a white and Gaussian process, whereas approximate difference equations for colored Gaussian inputs are proposed and tested. Stability conditions and residual MSE after convergence are also obtained. Agreement of theoretical results with those of simulation in the experiment with some examples of filter convergence shows sufficient accuracy of the theory and assures the usefulness of the difference equations in estimating filter performances, thus facilitating the design of adaptive filters using the NSRA     

Adaptive Nonlinear System Identification in the Short-Time Fourier Transform Domain

In this paper, we introduce an adaptive algorithm for nonlinear system identification in the short-time Fourier transform (STFT) domain. The adaptive scheme consists of a parallel combination of a linear component, represented by crossband filters between subbands, and a quadratic component, which is modeled by multiplicative cross-terms. We adaptively update the model parameters using the least-mean-square (LMS) algorithm, and derive explicit expressions for the transient and steady-state mean-square error (MSE) in frequency bins for white Gaussian inputs. We show that estimation of the nonlinear component improves the MSE performance only when the power ratio of nonlinear to linear components is relatively high. Furthermore, as the number of crossband filters increases, a lower steady-state MSE may be obtained at the expense of slower convergence. Experimental results support the theoretical derivations.     

The structure and design of realizable decision feedback equalizers for IIR channels with colored noise

A simple algorithm for optimizing decision feedback equalizers (DFEs) by minimizing the mean-square error (MSE) is presented. A complex baseband channel and correct past decisions are assumed. The dispersive channel may have infinite impulse response, and the noise may be colored. Consideration is given to optimal realizable (stable and finite-lag smoothing) forward and feedback filters in discrete time. They are parameterized as recursive filters. In the special case of transmission channels with finite impulse response and autoregressive noise, the minimum MSE can be attained with transversal feedback and forward filters. In general, the forward part should include a noise-whitening filter (the inverse noise model). The finite realizations of the filters are calculated using a polynomial equation approach to the linear quadratic optimization problem. The equalizer is optimized essentially by solving a system of linear equations Ax=B, where A contains transfer function coefficients from the channel and noise model. No calculation of correlations is required with this method. A simple expression for the minimal MSE is presented. The DFE is compared to MSE-optimal linear recursive equalizers. Expressions for the equalizer in the limiting case of infinite smoothing lags are also discussed.<>     

A modified non-negative LMS algorithm for online system identification

Adaptive filters are useful solutions for system identification problem where an optimization problem is used to formulated the estimation of the unknown model coefficients. The nonnegativity constraint is one of the most frequently used constraint which can be imposed to avoid physically unreasonable solutions and to comply with physical characteristics. In this letter, we propose a new variant of non-negative least mean square (NNLMS) that has a less mean square error (MSE) value and faster convergence rate. We provide both mean weight behavior and transient excess mean-square error analysis for proposed algorithm. Simulation results validate the theoretical analysis and show the effectiveness of our proposed algorithm.     

自适应加权FIR-Myriad混合滤波算法

针对FIR滤波器滤除脉冲噪声以及加权Myriad滤波器滤除高斯噪声的不足,提出基于FIR滤波器和加权WMy滤波器有效组合的一类新的非线性滤波器FIR-WMyH滤波器.利用神经网络中的反向传播算法,在均方误差准则下,推导了一个基于统计梯度的自适应算法.基于稳定α分布脉冲噪声模型下的仿真结果说明了该算法的良好的性能.     

Cyclostationary modeling, analysis, and optimal compensation ofquantization errors in subband codecs

The paper is concerned with the analysis and modeling of the effects of quantization of subband signals in subband codecs. Using cyclostationary representations, the authors derive equations for the autocorrelation and power spectral density (PSD) of the reconstructed signal y(n) in terms of the analysis/synthesis filters, the PSD of the input, and the pdf-optimized quantizer model. Formulas for the mean-square error (MSE) and for compaction gain are obtained in terms of these parameters. The authors constrain the filter bank to be perfect reconstruction (PR) (but not necessarily paraunitary) in the absence of quantization and transmission errors. These formulas set the stage for filter optimization (maximization of compaction gain and minimization of MSE) subject to PR and bit constraints. Optimal filters are designed, optimal compensation is performed, and the theoretical results are confirmed with simulations. The floating-point quantizer wherein only the mantissa is uniformly quantized is also analyzed and compared with the fixed point, pdf-optimized filter bank. For high bit rates, their performance is comparable     

On incremental sigma-delta modulation with optimal filtering

The paper presents a quantization-theoretic framework for studying incremental sigma-delta (/spl Sigma//spl Delta/) data conversion systems. The framework makes it possible to efficiently compute the quantization intervals and hence the transfer function of the quantizer, and to determine the mean square error (MSE) and maximum error for the optimal and conventional linear filters for first and second order incremental /spl Sigma//spl Delta/ modulators. The results show that the optimal filter can significantly outperform conventional linear filters in terms of both MSE and maximum error. The performance of conventional /spl Sigma//spl Delta/ data converters is then compared to that of incremental /spl Sigma//spl Delta/ with optimal filtering for bandlimited signals. It is shown that incremental /spl Sigma//spl Delta/ can outperform the conventional approach in terms of signal-to-noise-and-distortion ratio. The framework is also used to provide a simpler and more intuitive derivation of the Zoomer algorithm.     

基于整体退火遗传算法的柔性形态滤波器优化设计

整体退火遗传算法(WAGA)是一种新的遗传算法,它将退火机制引入选择算子,并允许父代加入竞争,使遗传算法更加稳健、高效.本文应用WAGA优化柔性形态滤波器,并研究了在不同噪声比例和最小平均绝对误差(MAE)、最小均方误差(MSE)准则下优化算法的性能.仿真实验显示优化后的柔性形态滤波器性能得到较大改善,适于各种噪声图像的滤波.     

Fixed-point error analysis of CORDIC processor based on the variance propagation formula

This paper presents a fixed-point mean-square error (MSE) analysis of coordinate rotation digital computer (CORDIC) processors based on the variance propagation method, whereas the conventional approaches provide only the error bound which results in large discrepancy between the analysis and actual implementation. The MSE analysis is aimed at obtaining a more accurate analysis of digital signal processing systems with CORDIC processor, especially when the design specification is given by the signal-to-noise ratio or MSE. For the MSE analysis, the error source and models are first defined and the output error is derived in terms of MSE in the rotation mode of the conventional CORDIC processor. It is shown that the proposed analysis can also be applied to the modified CORDIC algorithms. As an example of practical application, a fast Fourier transform processor using the CORDIC processor is presented in this paper, and its output error variance is analyzed with respect to the wordlength of CORDIC. The results show a close match between the analysis and simulation.     

VLSI implementation of a new LMS-based algorithm for noise removal in ECG signal

Least mean square (LMS)-based adaptive filters are widely deployed for removing artefacts in electrocardiogram (ECG) due to less number of computations. But they posses high mean square error (MSE) under noisy environment. The transform domain variable step-size LMS algorithm reduces the MSE at the cost of computational complexity. In this paper, a variable step-size delayed LMS adaptive filter is used to remove the artefacts from the ECG signal for improved feature extraction. The dedicated digital Signal processors provide fast processing, but they are not flexible. By using field programmable gate arrays, the pipelined architectures can be used to enhance the system performance. The pipelined architecture can enhance the operation efficiency of the adaptive filter and save the power consumption. This technique provides high signal-to-noise ratio and low MSE with reduced computational complexity; hence, it is a useful method for monitoring patients with heart-related problem.     

An Affine Combination of Two LMS Adaptive Filters—Transient Mean-Square Analysis

This paper studies the statistical behavior of an affine combination of the outputs of two least mean-square (LMS) adaptive filters that simultaneously adapt using the same white Gaussian inputs. The purpose of the combination is to obtain an LMS adaptive filter with fast convergence and small steady-state mean-square deviation (MSD). The linear combination studied is a generalization of the convex combination, in which the combination factor lambda(n) is restricted to the interval (0,1). The viewpoint is taken that each of the two filters produces dependent estimates of the unknown channel. Thus, there exists a sequence of optimal affine combining coefficients which minimizes the mean-square error (MSE). First, the optimal unrealizable affine combiner is studied and provides the best possible performance for this class. Then two new schemes are proposed for practical applications. The mean-square performances are analyzed and validated by Monte Carlo simulations. With proper design, the two practical schemes yield an overall MSD that is usually less than the MSDs of either filter.     

Tracking properties of a gradient-based second-order adaptive IIRnotch filter with constrained poles and zeros

Gradient-type adaptive IIR notch filters have many attractive merits for various real-life applications since they require a small number of computations and yet demonstrate practical performance. However, it is generally quite difficult to assess their performance analytically. Their tracking properties, in particular, have not yet been investigated. In this paper, the tracking performance of a plain gradient (PG) algorithm is analyzed in detail for a second-order adaptive IIR notch filter with constrained poles and zeros, which takes a linear chirp signal as its input. First, two sets of difference equations for the frequency tracking error and mean square error (MSE) are established in the sense of convergence in the mean and convergence in the mean square, respectively. Closed-form expressions for the asymptotic tracking error and MSE are then derived from these difference equations. An optimum step-size parameter for the algorithm is also evaluated based on the minimization of the asymptotic tracking error or the tracking MSE. It is discovered that the asymptotic tracking error may be driven to zero for a positive chirp rate by selecting a proper step size, which is an interesting property for a real-valued adaptive filtering algorithm. Extensive simulations are performed to support the analytical findings     

Convergence analysis of alias-free subband adaptive filters based on a frequency domain technique

Convergence analysis of alias-free subband adaptive filters (SADFs) is presented based on a frequency-domain technique where instead of analyzing the adaptive algorithms in the time-domain, the averaging method and the ordinary differential equation (ODE) method are applied to the frequency-domain expressions of the adaptive algorithms converted by the discrete Fourier transform. As an alias-free SADF algorithm, the SADF proposed by Pradhan and Reddy is known. In this paper, this technique is first applied to the Pradhan's SADF. The stability of the Pradhan's SADF is verified in the frequency domain, and a simple formula to evaluate the mean square error (MSE) of the algorithm is theoretically derived. By using a slight modification, the technique can be applied to the two-band delayless subband adaptive filter (DLSADF) with the Hadamard transform. The stability condition and the MSE of the DLSADF with the Hadamard transform are also obtained. Simulation results of both algorithms show the validity of the theoretical results.     

Suppression of transients in variable recursive digital filterswith a novel and efficient cancellation method

A new method for suppressing transients in recursive infinite impulse response (IIR) digital filters is proposed. The technique is based on modifying the state (delay) variables of the filter when coefficients are changed so that the filter enters a new state smoothly without transient attacks, as originally proposed by Zetterberg and Zhang (1988). In this correspondence, we modify the Zetterberg-Zhang algorithm to render it feasible for efficient implementation. We define a mean square error (MSE) measure for transients and determine the optimal transient suppressor to cancel the transients down to a desired level at the minimum complexity of implementation. The application of the method to all-pole and direct-form II (DF II) IIR filter sections is studied in detail. Time-varying recursive filtering with transient elimination is illustrated for tunable fractional delay filters and variable-bandwidth lowpass filters     

用于运动估计的最小化MSE滤波器设计

根据估计量的统计特性,提出了一种适用于低信噪比条件下运动估计的最小化MSE(均方误差)滤波器多尺度运动估计算法.首先,根据Cramer-Rao下界建立一个包含估计量噪声项的MSE惩罚函数.然后,最小化MSE惩罚函数设计一种用于低信噪比条件下运动估计的优化滤波器.该优化滤波器与多尺度方法相结合,使其对低信噪比条件下运动估计的精度得到了进一步提高.实验模拟表明,该方法在估计2个像素附近的噪声图像运动时,估计偏差小于0.008个像素.与传统方法相比,本文方法对低信噪比条件下的运动估计具有更高的估计精度.     

Adaptive reduced-rank LCMV beamforming algorithms based on joint iterative optimization of filters: Design and analysis

This paper presents reduced-rank linearly constrained minimum variance (LCMV) beamforming algorithms based on joint iterative optimization of filters. The proposed reduced-rank scheme is based on a constrained joint iterative optimization of filters according to the minimum variance criterion. The proposed optimization procedure adjusts the parameters of a projection matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe LCMV expressions for the design of the projection matrix and the reduced-rank filter. We then describe stochastic gradient and develop recursive least-squares adaptive algorithms for their efficient implementation along with automatic rank selection techniques. An analysis of the stability and the convergence properties of the proposed algorithms is presented and semi-analytical expressions are derived for predicting their mean squared error (MSE) performance. Simulations for a beamforming application show that the proposed scheme and algorithms outperform in convergence and tracking the existing full-rank and reduced-rank algorithms while requiring comparable complexity.     

Optimal filter banks for signal reconstruction from noisy subbandcomponents

Conventional design techniques for analysis and synthesis filters in subband processing applications guarantee perfect reconstruction of the original signal from its subband components. The resulting filters, however, lose their optimality when additive noise due, for example, to signal quantization, disturbs the subband sequences. We propose filter design techniques that minimize the reconstruction mean squared error (MSE) taking into account the second order statistics of signals and noise in the case of either stochastic or deterministic signals. A novel recursive, pseudo-adaptive algorithm is proposed for efficient design of these filters. Analysis and derivations are extended to 2-D signals and filters using powerful Kronecker product notation. A prototype application of the proposed ideas in subband coding is presented. Simulations illustrate the superior performance of the proposed filter banks versus conventional perfect reconstruction filters in the presence of additive subband noise     

Steady-state analysis of a complex adaptive notch filter using modified gradient algorithm

Based on power spectral density (PSD) analytical technique, mean square error (MSE) (or variance) of the frequency estimate of a first-order complex adaptive IIR notch filter (ANF) using modified complex plain gradient (MCPG) algorithm is investigated in this paper. The steady-state expression for MSE is derived in closed form. A quantitative analysis for the estimation MSE has been carried out. It has been revealed that the MSE of frequency estimate is independent of an input frequency of a complex sinusoid. In addition, computer simulations are treated to corroborate the theoretical analysis and the relationships between MSE and system parameters are shown.     

IIR Filter Adaptation Using Branch-and-Bound: A Novel Approach

Adaptive infinite impulse response (IIR) filters provide significant advantages over equivalent finite impulse response (FIR) implementations because they are able to more accurately model physical plants that have pole-zero structures. Additionally, they are typically capable of meeting performance specifications using fewer filter parameters. This savings in parameters, which can be as much as 5–10 times, leads to the use of fewer multiplier blocks and therefore, lower power consumption. Despite these advantages, adaptive IIR filters have not found widespread use because the associated mean squared error (MSE) cost function is multimodal and therefore, significantly difficult to minimize. Additionally, the filter can become unstable during adaptation. These two properties pose several problems for adaptive algorithms, causing them to be sensitive to initial conditions, produce biased solutions, unstable filter configurations or converge to local minima. These problems prevent the widespread use of adaptive IIR filters in practice and if such filter structures are to become more practical, new, innovative solutions are required. This paper proposes a new algorithm for minimizing the MSE cost function of adaptive IIR filters aimed at addressing some of the aforementioned issues. We adopt the approach of using a Branch-and-Bound algorithm, which is an exhaustive search method, and employ interval arithmetic for all computations. Simulation results show that the resulting algorithm is viable and competitive and, when compared with a number of existing state-of-the-art algorithms, outperforms them in terms of the MSE of the final point.