A minimum misadjustment adaptive FIR filter

The performance of an adaptive filter is limited by the misadjustment resulting from the variance of adapting parameters. This paper develops a method to reduce the misadjustment when the additive noise in the desired signal is correlated. Given a static linear model, the linear estimator that can achieve the minimum parameter variance estimate is known as the best linear unbiased estimator (BLUE). Starting from classical estimation theory and a Gaussian autoregressive (AR) noise model, a maximum likelihood (ML) estimator that jointly estimates the filter parameters and the noise statistics is established. The estimator is shown to approach the best linear unbiased estimator asymptotically. The proposed adaptive filtering method follows by modifying the commonly used mean-square error (MSE) criterion in accordance with the ML cost function. The new configuration consists of two adaptive components: a modeling filter and a noise whitening filter. Convergence study reveals that there is only one minimum in the error surface, and global convergence is guaranteed. Analysis of the adaptive system when optimized by LMS or RLS is made, together with the tracking capability investigation. The proposed adaptive method performs significantly better than a usual adaptive filter with correlated additive noise and tracks a time-varying system more effectively     

A flexible adaptive FIR filter VLSI IC

The architecture and features of the Motorola DSP56200 are described. The DSP56200 is an algorithm-specific cascadable digital signal processing peripheral designed to perform the computationally intensive tasks associated with finite impulse response (FIR) and adaptive FIR digital filtering applications. The DSP56200 is implemented in high-performance, low-power 1.5-μm HCMOS technology and is available in a 28-pin DIP package. The on-chip computation unit includes a 97.5-ns 24-bit×24-bit coefficient RAM, and a 256-bit×16-bit data RAM. Three modes of operation allow the part to be used as a single, dual, or single adaptive FIR filter, with up to 256 taps per chip. In the adaptive mode, the part performs the FIR filtering and least-mean-square (LMS) coefficient update operations for a single tap in 195 ns, permitting use of the part as a 19-kHz sampling rate, 256-tap adaptive FIR filter. A programmable DC tap, coefficient leakage, and adaptation coefficient parameters in the adaptive FIR mode allow the DSP56200 to be used in a wide variety of adaptive FIR filtering applications. The performance of the part in an echo canceler configuration is presented. Typical applications of the part are also described     

Efficient least squares adaptive algorithms for FIR transversalfiltering

A unified view of algorithms for adaptive transversal FIR filtering and system identification has been presented. Wiener filtering and stochastic approximation are the origins from which all the algorithms have been derived, via a suitable choice of iterative optimization schemes and appropriate design parameters. Following this philosophy, the LMS algorithm and its offspring have been presented and interpreted as stochastic approximations of iterative deterministic steepest descent optimization schemes. On the other hand, the RLS and the quasi-RLS algorithms, like the quasi-Newton, the FNTN, and the affine projection algorithm, have been derived as stochastic approximations of iterative deterministic Newton and quasi-Newton methods. Fast implementations of these methods have been discussed. Block-adaptive, and block-exact adaptive filtering have also been considered. The performance of the adaptive algorithms has been demonstrated by computer simulations     

Eigenstructure algorithms for multirate adaptive lossless FIR filters

This paper addresses the problem of adaptively optimizing a two-channel lossless finite-impulse-response (FIR) filter bank, which finds application in subband coding and wavelet signal analysis. Instead of using a gradient decent procedure-with its inherent problem of becoming trapped in local minima of a nonquadratic cost function-two eigenstructure algorithms are proposed. Both algorithms feature a priori bounds on the output variance at any convergent point, which, based on simulations, lead to solutions that lie acceptably close to a global minimum point of an output variance objective function. Moreover, a sufficient condition for such stationary points based on fixed-point theory is shown. It is shown that the convergence rate of both algorithms increases as the separation of eigenvalues of the input covariance matrix increases. Simulations for synthetic and real data support the conclusions.     

Robust least mean square adaptive FIR filter algorithm

The authors propose a new robust adaptive FIR filter algorithm for system identification applications based on a statistical approach named the M estimation. The proposed robust least mean square algorithm differs from the conventional one by the insertion of a suitably chosen nonlinear transformation of the prediction residuals. The effect of nonlinearity is to assign less weight to a small portion of large residuals so that the impulsive noise in the desired filter response will not greatly influence the final parameter estimates. The convergence of the parameter estimates is established theoretically using the ordinary differential equation approach. The feasibility of the approach is demonstrated with simulations     

Bit-serial VLSI implementation of delayed LMS adaptive FIR filters

The delayed least-mean-square (DLMS) algorithm is useful for adaptive finite impulse response (FIR) filtering applications where high throughput rates are required. In the paper, a bit-serial bit-level systolic array based on new schemes for multiplication and inner-product computation is presented to implement DLMS adaptive N-tap FIR filters. The architecture is highly regular, modular, and thus well-suited to VLSI implementation. It has an efficiency of 100% and a throughput rate of one filter output per 2B cycles, where B is the word length of input data. In addition, the proposed array uses a small delay of [(4B+N/2+4)/2B] in the filter coefficient adaptation, where [x] is the smallest integer greater than or equal to x. This ensures that the DLMS algorithm can have good performance under proper selection of the step size. Based on a conservative design technique of static complementary metal oxide semiconductor (CMOS) logic, it is shown that the proposed system can be realized in a single chip for most practical applications     

Improved Rao-Blackwellized H∞ filter based mobile robot SLAM

For the problems of estimation accuracy, inconsistencies and robustness in mobile robot simultaneous localization and mapping (SLAM), a novel SLAM based on improved Rao-Blackwellized H∞ particle filter (IRBHF-SLAM) algorithm is proposed. The iterated unscented H∞ filter (IUHF) is utilized to accurately calculate the importance density function, repeatedly correcting the state mean and the covariance matrix by the iterative update method. The laser sensor’s observation information is introduced into sequential importance sampling routine. It can avoid the calculation of Jacobian matrix and linearization error accumulation; meanwhile, the robustness of the algorithm is enhanced. IRBHF-SLAM is compared with FastSLAM2.0 and the unscented FastSLAM (UFastSLAM) under different noises in simulation experiments. Results show the algorithm can improve the estimation accuracy and stability. The improved approach, based on the robot operation system (ROS), runs on the Pioneer3-DX robot equipped with a HOKUYO URG-04LX (URG) laser range finder. Experimental results show the improved algorithm can reduce the required number of particles and the operating time; and create online 2 dimensional (2-D) grid-map with high precision in different environments.     

A pipelined LMS adaptive FIR filter architecture without adaptationdelay

Past methods for mapping the least-mean-square (LMS) adaptive finite-impulse-response (FIR) filter onto parallel and pipelined architectures either introduce delays in the coefficient updates or have excessive hardware requirements. We describe a hardware-efficient pipelined architecture for the LMS adaptive FIR filter that produces the same output and error signals as would be produced by the standard LMS adaptive filter architecture without adaptation delays. Unlike existing architectures for delayless LMS adaptation, the new architecture's throughput is independent of the filter length     

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.     

FIR自适应滤波的语音增强算法

提出一种基于线性预测FIR自适应滤波的语音增强算法,该算法可实时过滤被噪声污染的语音信号,提高信噪比,从而提高语音识别系统的识别率。仿真结果证明该算法具有较好的降噪效果。     

Delayed N-path structure for high speed adaptive FIR digital filtering

A structure and its algorithm for high speed adaptive FIR digital filtering based on the delayed N-path concept is presented. Using N/sup 2/ processors, the throughput rate of the proposed filter can be N/sup 2/ times faster than that of the same filter realised directly using one such processor.<>     

Sidelobe reduction via adaptive FIR filtering in SAR imagery.

The paper describes a class of adaptive weighting functions that greatly reduce sidelobes, interference, and noise in Fourier transform data. By restricting the class of adaptive weighting functions, the adaptively weighted Fourier transform data can be represented as the convolution of the unweighted Fourier transform with a data adaptive FIR filter where one selects the FIR filter coefficients to maximize signal-to-interference ratio. This adaptive sidelobe reduction (ASR) procedure is analogous to Capon's (1969) minimum variance method (MVM) of adaptive spectral estimation. Unlike MVM, which provides a statistical estimate of the real-valued power spectral density, thereby estimating noise level and improving resolution, ASR provides a single-realization complex-valued estimate of the Fourier transform that suppresses sidelobes and noise. Further, the computational complexity of ASR is dramatically lower than that of MVM, which is critical for large multidimensional problems such as synthetic aperture radar (SAR) image formation. ASR performance characteristics can be varied through the choice of filter order, l(1)- or l(2)-norm filter vector constraints and a separable or nonseparable multidimensional implementation. The author compares simulated point scattering SAR imagery produced by the ASR, MVM, and MUSIC algorithms and illustrates ASR performance on three sets of collected SAR imagery.     

基于导频信道估计的MIMO同步算法

针对多输入多输出（MIMO）技术的应用需要,研究了MIMO同步算法,提出了一种基于导频和信道估计的联合同步算法。该方法运用基于二阶矩的盲估计算法进行信道估计;再把信道估计的结果用最小均方误差准则做均衡,并设计一种特别的导频来估计载波频偏并估计信道时延。在不同条件下进行仿真,分析结果表明：在复杂信道环境下算法的性能相比传统算法有显著提高,研究结果对MIMO的工程应用有较好参考意义。     

In the optimum design of the block adaptive FIR digital filter

A general optimum block adaptive (GOBA) algorithm for adaptive FIR (finite impulse response) filtering is presented. In this algorithm, the correction terms for the filter coefficients in each block, instead of the convergence factors, are optimized in a least squares sense. There are no constraints on the block length L and the filter tap number N. It is shown that the GOBA algorithm is reduced to the normalized LMS algorithm when L⩾N. The convergence of the GOBA algorithm can be assured if the correlation matrix of the input signal is positive definite. Computer simulations based on an efficient computing procedure confirm that the GOBA algorithm achieves faster convergence with slightly degraded convergence accuracy in stationary environments and better weight tracking capability in nonstationary environments as compared to existing block adaptive algorithms with no constraints on L and N     

HRLS: a more efficient RLS algorithm for adaptive FIR filtering

The fast convergence rate and its immunity to the eigenvalue spread of the input correlation matrix make the RLS algorithm particularly attractive. However, the computational complexity is high. We propose using a hierarchical approach to reduce the computational complexity and further increase the convergence rate. The results of simulation runs and theoretical justifications confirm our claims     

基于自适应神经网络的二维线性相位FIR滤波器优化设计

提出一种基于自适应三角函数基神经网络的二维线性相位FIR滤波器优化设计方法.该方法根据二维线性相位FIR滤波器幅频响应特性,采用三角函数基神经网络优化算法计算滤波器系数,同时在神经网络训练过程引入自适应学习率算法,提高神经网络的学习效率和收敛速度.通过训练神经网络的权值,使二维线性相位FIR滤波器幅频响应与理想幅频响应...     

Blind fractionally spaced equalization of noisy FIR channels:direct and adaptive solutions

Blind fractionally spaced equalizers reduce intersymbol interference using second-order statistics without the need for training sequences. Methods for finding FIR zero-forcing blind equalizers directly from the observations are described, and adaptive versions are developed. In contrast, most current methods require channel estimation as a first step to estimating the equalizer. The direct methods can be zero-forcing, minimum mean-square error, or even minimum mean square error (MMSE) within the class of zero-forcing equalizers. Performance of the proposed methods and comparisons with existing approaches are shown for a variety of channels, including an empirically measured digital microwave channel     

Robust H∞ filter design for uncertain linear systems withmultiple time-varying state delays

The problem of robust H∞ filtering for continuous-time uncertain linear systems with multiple time-varying delays in the state variables is investigated. The uncertain parameters are supposed to belong to a given convex bounded polyhedral domain. The aim is to design a stable linear filter assuring asymptotic stability and a prescribed H∞ performance level for the filtering error system, irrespective of the uncertainties and the time delays. Sufficient conditions for the existence of such a filter are established in terms of linear matrix inequalities, which can be efficiently solved by means of powerful convex programming tools with global convergence assured. An example illustrates the proposed methodology     

Quantifying the effects of dimension on the convergence rate of theLMS adaptive FIR estimator

The convergence rate of an LMS adaptive FIR filter to an unknown stationary channel may be influenced by the filter parameter dimension as well as by the input signal's characteristics. This dimension influence may be of importance in applications, such as adaptive acoustic echo cancellation, in which the unknown channel is typically modeled as a “long” FIR filter. The paper includes the development and proposal of a novel measure of the expected convergence rate of the LMS/FIR filter followed by analysis of this convergence rate measure. The analysis indicates that unless the input signal is white, the expected convergence rate decreases with increasing dimension down to a limiting value, which is determined by the input signal's autocorrelation level     

Reducing data dependent jitter utilising adaptive FIR pre-emphasis in 0.18 μm CMOS

Due to advances of technology in multimedia applications in recent years, the demand for high user end bandwidth point to point links has increased significantly. Jitter requirements have become ever more stringent with the increase in high speed serial link data rates. The introduced jitter severely degrades the performance of the high speed serial link. This paper introduces an adaptive FIR pre-emphasis technique as a means to alleviate the problem of limited off-chip bandwidth introducing data dependant jitter. Mathematical as well as SPICE simulation results are presented, together with the implemented integrated circuit layouts of the novel 0.18 μm CMOS implementation. Limited results from the experimentally tested IC are also presented and discussed. The adaptive pre-emphasis technique employed results in a simulated data dependant jitter reduction to less than 12.5% of a unit interval at a data rate of 5 Gb/s and a modelled 30″ FR-4 backplane copper channel.