Adaptive Finite Impulse Response Filter Research Articles

A fast convergence algorithm for sparse-tap adaptive FIR filters identifying an unknown number of dispersive regions

This paper proposes a fast convergence algorithm for sparse-tap adaptive finite impulse response (FIR) filters to identify an unknown number of multiple dispersive regions. Coefficient values and tap-positions of the adaptive filter are simultaneously controlled. A constrained region for new-tap positions is selected from equisize subgroups of all possible tap-positions, and it hops from one subgroup to another to cover multiple dispersive regions. The hopping order and the stay time for each subgroup are adaptively determined based on the absolute coefficient values. Simulation results with colored signals show that the proposed algorithm saves more than 80% in the convergence time over the full-tap NLMS and 50% over the STWQ. Tracking capability of the proposed algorithm exhibits its superior characteristics. These characteristics are confirmed by hardware evaluations with a telephone network simulator.

Noisy input–output system identification approach for time delay estimation

An unbiased impulse response estimation approach is applied to time delay estimation between noisy signals received at two spatially separated sensors. The estimated delay is modeled by an adaptive finite impulse response filter whose coefficients are updated according to a modified least mean square (LMS) scheme. Simulation results show that the proposed algorithm outperforms the conventional LMS time delay estimator for autoregressive signals as well as real speech data.

Nonlinear FIR adaptive filters with a gradient adaptive amplitude in the nonlinearity

A nonlinear gradient descent (NGD) learning algorithm with an adaptive amplitude of the nonlinearity is derived for the class of nonlinear finite impulse response (FIR) adaptive filters (dynamical perceptron). This is based on the adaptive amplitude backpropagation (AABP) algorithm for large-scale neural networks. The amplitude of the nonlinear activation function is made gradient adaptive to give the adaptive amplitude nonlinear gradient descent (AANGD) algorithm, making the AANGD suitable for processing nonlinear and nonstationary input signals with a large dynamical range. Experimental results show the AANGD algorithm outperforming the standard NGD algorithm on both colored and nonlinear input with large dynamics. Despite its simplicity, the considered algorithm proves suitable for adaptive filtering of nonlinear and nonstationary signals.

Nonparametric spectral analysis of gapped data via an adaptive filtering approach

We present an algorithm for nonparametric complex spectral analysis of gapped data via an adaptive finite impulse response (FIR) filtering approach, referred to as the gapped-data amplitude and phase estimation (GAPES) algorithm. The incomplete data sequence may contain gaps of various sizes. The GAPES algorithm iterates the following two steps: (1) estimating the adaptive FIR filter and the corresponding complex spectrum via amplitude and phase estimation (APES), a nonparametric adaptive FIR filtering approach, and (2) filling in the gaps via a least-squares APES fitting criterion. The initial condition for the iteration is obtained from the available data segments via APES. Numerical results are presented to demonstrate the effectiveness of the proposed GAPES algorithm.

Multistage adaptive higher-order nonlinear finite impulse response filters for chaotic time series predictions

A multistage adaptive higher-order nonlinear finite impulse response (MAHONFIR) filter is proposed to predict chaotic time series. Using this approach, we may readily derive the decoupled parallel algorithm for the adaptation of the coefficients of the MAHONFIR filter, to guarantee a more rapid convergence of the adaptive weights to their optimal values. Numerical simulation results show that the MAHONFIR filters proposed here illustrate a very good performance for making an adaptive prediction of chaotic time series.

New adaptive filtering algorithms based on an orthogonal projection of gradient vectors

In this letter, we propose two algorithms for adaptive finite impulse response (FIR) filters as variants of the filtered gradient adaptive (FGA) algorithm. The proposed algorithms are formulated from the FGA by introducing an orthogonal constraint between the direction vectors. The algorithms employ the forgetting factor optimized on a sample-by-sample basis so that the direction vector is orthogonal to the previous direction vector, while the FGA algorithm uses a fixed one. It is shown through the computer simulations that the proposed algorithms are superior in terms of convergence and tracking capability to the FGA algorithm.

Adaptive inverse control algorithm for shock testing

An adaptive inverse control algorithm is proposed for shock testing an arbitrary specimen using an electrodynamic actuator. The purpose is to ascertain whether the specimen can survive and continue to function under severe shock conditions. The main difficulty in shock control is that the specimen dynamics vary significantly and a control algorithm is required that adapts to the characteristics of a new specimen. The control algorithm used is the adaptive inverse control method which approximates an inverse model of the loaded shaker with a finite impulse response adaptive filter, such that the reference input is reproduced at the shaker output. The standard filtered-x least mean square control structure used in the adaptive inverse control algorithm is modified to a block-processing structure, with the frequency-domain adaptive filter as the adaptation algorithm. Practical results show that the filtered-x frequency-domain adaptive filter control algorithm allows convergence of the shaker output to the assigned reference shock pulse.

Quantifying the convergence speed of LMS adaptiveFIRfilter with autoregressive inputs

In general the least mean squares adaptive finite impulse response (FIR) filter converges more slowly with an increase in filter length and input signal correlation level. An explicit expression is presented relating the convergence speed of this adaptive filter to the FIR filter length and the correlation characteristics of autoregressive (AR) modelled input signals. The expression provides a simple means for justifying (or not) the cost of input signal whitening techniques within for example acoustic echo cancellation, in which very large FIR filter lengths and highly correlated AR modelled speech input signals occur.

A comparison of white test signals used in active sound cancellation

Several different test signals with flat power spectrum, such as white noise, Schroeder-phased waveform, and Galois noise sequences were generated and used to adaptively model the error path in an active sound cancellation (ASC) controller. The standard filtered-X LMS (FXLMS) algorithm was used with a Finite Impulse Response (FIR) model of the error path and an FIR controller structure. Both adaptive FIR filters were operated during the active sound cancellation of 80 and 125 Hz tones. The residual modeling error for different test signal mean power spectral densities (PSD) was determined. It was discovered that the residual error decreased with an increase in the mean PSD, until a minimum residual error was achieved. When the mean PSD was increased beyond this value, no further decrease in residual error occurred. Each test signal was distinguished by the minimum mean PSD that achieved the minimum residual error. All three test signals achieved approximately the same residual error at approximately the same mean PSD for both the 80 and 125 Hz tone. Therefore, the best test signal was selected as the signal with the lowest peak factor, in this case the Schroeder-phased waveform. Given a low peak factor, power can be injected into the test waveform without significantly increasing the size of the time domain envelope.

Removal of cardiac beat artifact in oesophageal pressure measurement by frequency analysis

Oesophageal pressure (Pes) measurements are important in medical research and useful in clinical diagnosis. Measurements, however, are contaminated heavily by cardiac artifacts. The spectrum and waveform of the Pes signal is obtained from the oesophageal balloon. Adaptive finite impulse response (AFIR) filter and modified adaptive noise cancellation (MANC) methods are adopted to filter out cardiac beat interference. These results are compared. In the frequency domain, frequency variations and spectral overlap between the Pes components and cardiac beat signal components impact on the performance of the filter. From our experimental results on power strength, the fourth or higher harmonics did not have any significant effect on the filter performance. However, the second harmonics of these signals had a significant effect on the filtering result. Thus, in the design of AFIR filters, attention is needed to minimise these effects. In frequency analysis, these harmonics or overlapping frequencies do not affect MANC. MANC was the better method for eliminating cardiac beat artifact in Pes measurement. The dynamic compliance (Cdyn) was also used to evaluate the performance of MANC and AFIR. The standard deviation of Cdyn was less than 0.15 using MANC, compared with standard deviations as high as 0.57 for AFIR. We conclude that MANC performs better than AFIR.

Robust adaptive filtering algorithms for α-stable random processes

A new class of algorithms based on the fractional lower order statistics is proposed for finite impulse response adaptive filtering in the presence of a stable processes. It is shown that the normalized least mean p-norm (NLMP) and Douglas' family of normalized least mean square algorithms are special cases of the proposed class of algorithms. A convergence proof for the new algorithm is given by showing that it performs a descent-type update of the NLMP cost function. Simulation studies indicate that the proposed algorithms provide superior performance in impulsive noise environments compared to the existing approaches.

A realization of 2-D adaptive filters using affine projection algorithm

This paper proposes a realization of two-dimensional (2-D) adaptive finite impulse response (FIR) filters using affine projection method. The convergence property of the proposed algorithm is superior to that of the 2-D least mean square (LMS) and normalized LMS (NLMS) algorithms. This algorithm has properties that lie between those of NLMS and recursive least squares (RLS) algorithms. The convergence of this filter can be proved. The generalization of the proposed algorithm is also discussed. To illustrate the utility of the proposed technique, this filter is applied to the 2-D system identification.

Wiener-filter-based Minimum Variance Self-tuning Regulation

This paper presents a new direct discrete-time self-tuning minimum variance control scheme for not necessarily minimum-phase SISO systems that are operating in a stochastic environment. It is assumed that the open-loop system is asymptotically stable, or can be stabilized by a fixed controller, and that the noise model satisfies the standard SPR properties. No assumptions are made regarding the zero polynomial of the controlled plant. The proposed direct self-tuning scheme consists of two-parameter adaptation algorithms (PAA) running simultaneously. The first PAA is an adaptive whitening filter. The adaptive whitening filter error residual is fed into an adaptive finite impulse response (FIR) filter, which generates the control signal. The second PAA estimates the parameters of the optimal Wiener FIR filter which minimizes the variance of the system output. A complete stability analysis and a discussion of the convergence and self-tuning properties of the proposed self-tuning scheme is included.

A pipelined LMS adaptive FIR filter architecture without adaptation delay

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.

Efficient postprocessor for blocky effect removal based on transform characteristics

In this paper, we propose transform-domain algorithms to effectively classify the characteristics of blocks and estimate the strength of the blocky effect. The transform-domain algorithms require much lower computational complexity and much less memory than the spatial ones. Along with the estimated blocky strength, we also propose an adaptive finite impulse response (FIR) filter to effectively remove the blocky effect. Simulation results show that the proposed algorithms sharply preserve the edge information and greatly improve the decoded image quality.

A low-power 128-tap digital adaptive equalizer for broadband modems

This paper describes a modem receiver chip containing two 64-tap adaptive finite impulse response (FIR) filters configured in parallel as in-phase and quadrature-phase filters. Each filter has a span of 16 symbols and can be configured for T/2, T/3, or T/4 fractional spacing. A zero-latency pipeline technique is used that allows adaptive filters of arbitrary length without degrading the speed. Power is saved at the algorithmic, architectural, and circuit levels. The chip has support for dynamically tuning coefficient precision, updating rates and filter lengths to reduce power consumption. The chip was fabricated in 0.5-/spl mu/m CMOS technology and consumes 535 mW of power when operating at 50 MHz with 128 taps, T/4 spacing, and symbol-rate power-of-two LMS updating. This can be further reduced to 280 mW using dynamic power reduction techniques. The power in the FIR filter is 162 mW with maximum precision converged coefficients which corresponds to 5.1 mW per multiply-accumulate operation.

Control of Broadband Acoustic Radiation with Adaptive Structures

Active Structural Acoustic Control has been demonstrated for harmonic disturbances on various structures. This paper presents an extension of this work to the attenuation of acoustic radiation from planar structures subject to broadband disturbances. An adaptive, multiinput multi-output (MIMO), feedforward broadband acoustic control system has been developed. The control approach is the least mean squares (LMS) algorithm. The compensators are adaptive finite impulse response (FIR) filters. The control inputs are implemented with piezoceramic actuators. Both far-field microphones and polyvinylidene fluoride (PVDF) distributed structural sensors are used as error sensors. Both the actuator locations and the PVDF sensor configurations were optimally chosen to minimize radiated sound power assuming multiple frequency excitation. The disturbance is band-limited zero mean white noise and is implemented with a point force shaker. In the control of harmonically excited systems, satisfactory attenuation is possible with a single-input single-output (SISO) controller. In contrast, for systems excited with broadband disturbances, a MIMO controller is necessary for significant acoustic attenuation. Experimental results for the control of a simply supported plate are presented.

Slow asymptotic convergence of LMS acoustic echo cancelers

In most acoustic echo canceler (AEC) applications, an adaptive finite impulse response (FIR) filter is employed with coefficients that are computed using the LMS algorithm. The paper establishes a theoretical basis for the slow asymptotic convergence that is often noted in practice for such applications. The analytical approach expresses the mean-square error trajectory in terms of eigenmodes and then applies the asymptotic theory of Toeplitz matrices to obtain a solution that is based on a general characterization of the actual room impulse response. The method leads to good approximations even for a moderate number of taps (N>16) and applies to both full-band and subband designs. Explicit mathematical expressions of the mean-square error convergence are derived for bandlimited white noise, a first-order Markov process, and, more generally, pth-order rational spectra and a direct power-law model, which relates to lowpass FIR filters. These expressions show that the asymptotic convergence is generally slow, being at best of order 1/t for bandlimited white noise. It is argued that input filter design cannot do much to improve slow convergence. However, the theory suggests postfiltering as a remedy that would be useful for the full-band LMS AEC and may also be applicable to subband designs.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A two-dimensional adaptive lattice (TDAL) structure for noise cancelling

One-dimensional adaptive lattice (1-D AL) filters have been used in a wide range of signal processing applications because they provide convergence advantages not obtainable with conventional adaptive finite impulse response (FIR) filters. To extend these advantages to two-dimensional signal processing, a proposed structure for the two-dimensional adaptive lattice (TDAL) is presented. The TDAL structure, of order L × L, consists of L rows of the linear prediction lattice (LPL), each LPL is of order L— 1 and is a function of z 2. The signal input to the ith LPL is processed by z 1 −1 where 0≤i≤−1 The LMS adaptive algorithm, which is easy lo implement, is used to update the structure coefficients. Analysis and experiments are provided to prove the advantages of the proposed structure over the conventional adaptive 2-D FIR structure.

Tracking random walk systems with vector space adaptive filters

Tracking properties are considered for adaptive filters that are adjusted within a vector space of filtering operations. Such vector space adaptive filters have both the desirable convergence properties of adaptive finite impulse response filters, and additionally some of the modeling flexibility of adaptive infinite impulse response filters. For a given vector space of systems, the adaptive filter structure is determined by a choice of basis for the vector space. Basis dependent expressions are developed for the asymptotic mean square error under least mean square, recursive least square, and Kalman filtering adaptation, when the optimal filter specification is subject to random walk variations. It is shown that under these conditions, the minimum achievable mean square error using least mean square adaptation is equal to the optimal value provided by the Kalman filtering algorithm, but that recursive least squares adaptation does not in general attain this minimum error.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>