In real-world active noise control (ANC) applications, disturbance can be picked up by error sensors and significantly degrade the steady-state ANC performance. This study proposes two techniques in combination with a...
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In real-world active noise control (ANC) applications, disturbance can be picked up by error sensors and significantly degrade the steady-state ANC performance. This study proposes two techniques in combination with a least-mean-square (lms) based ANC algorithm, named normalized filtered-x lms/commutation error (NFxlms/CE) algorithm, to deal with the disturbance that is independent of a reference signal. A new stochastic method to analyze convergence properties of the NFxlms/CE algorithm under influence of the disturbance is first established. Given that the reference signal is persistently exciting of sufficient order, exponential convergence of the algorithm is derived with a step-size condition. An exponential-decay step size (EDSS) is then proposed to obtain a new ANC algorithm referred to as EDSS-NFxlms/CE algorithm. In addition, a disturbance-compensation (DC) technique is developed for the EDSS-NFxlms/CE algorithm to obtain an EDSS-NFxlms/CE_DC algorithm such that the influence of the disturbance can be reduced. It is shown that the EDSS-NFxlms/CE_DC algorithm is exponentially convergent. Moreover, computer simulations show that the EDSS-NFxlms/CE_DC algorithm can achieve a better ANC performance in terms of convergence rate and level of noise reduction as compared with that using the EDSS-NFxlms/CE algorithm without DC and that using NFxlms/CE_DC algorithm of constant step sizes. These results support the effectiveness of the proposed techniques and EDSS-NFxlms/CE_DC algorithm. Copyright (c) 2012 John Wiley & Sons, Ltd.
The FXlms algorithm, which is extensively used in active noise control, exhibits frequency dependent convergence behavior. This leads to degraded performance for time-varying and multiple frequency signals. A new algo...
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The FXlms algorithm, which is extensively used in active noise control, exhibits frequency dependent convergence behavior. This leads to degraded performance for time-varying and multiple frequency signals. A new algorithm called the eigenvalue equalization filtered-x least mean squares (EE-FXlms) has been developed to overcome this limitation without increasing the computational burden of the controller. The algorithm is easily implemented for either single or multichannel control. The magnitude coefficients of the secondary path transfer function estimate are altered while preserving the phase. For a reference signal that has the same magnitude at all frequencies, the secondary path estimate is given a flat response over frequency. For a reference signal that contains tonal components of unequal magnitudes, the magnitude coefficients of the secondary path are adjusted to be the inverse magnitude of the reference tones. Both modifications reduce the variation in the eigenvalues of the filtered-x autocorrelation matrix and lead to increased performance. Experimental results show that the EE-FXlms algorithm provides 3.5-4.4 dB additional attenuation at the error sensor compared to normal FXlms control. The EE-FXlms algorithm's convergence rate at individual frequencies is faster and more uniform than the normal FXlms algorithm with several second improvement being seen in some cases. (c) 2008 Acoustical Society of America.
The paper analyzes the transient and steady-state performances of a least mean square algorithm in the rarely-studied situation of a time-varying input power. A scenario of periodic pulsed variation of the input power...
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The paper analyzes the transient and steady-state performances of a least mean square algorithm in the rarely-studied situation of a time-varying input power. A scenario of periodic pulsed variation of the input power is considered. The analysis is carried out in the context of tracking a Markov plant with a white Gaussian input. It is shown that the mean square deviation (MSD) converges to a periodic sequence having the same period as that of the variation of the input power. Expressions are derived for the convergence time and the steady-state peak MSD. Surprisingly, it is found that neither the transient performance nor the steady-state performance degrades with rapid variation of the input power. On the other hand, slow input power variation causes degradation in both the transient and steady-state performances for given amplitude of variation of the input power. In the case of a time-invariant plant, neither rapid nor slow variation of the input power causes degradation in the steady-state performance. On the other hand, there is degradation in the transient performance for slow variation of the input power. Copyright (C) 2012 John Wiley & Sons, Ltd.
An analysis of a near-end crosstalk (NEXT) cancelation system that uses adaptive digital filters is described. The analysis is based on two well-known models for the NEXT coupling factor, the Bradley and Lin models, a...
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An analysis of a near-end crosstalk (NEXT) cancelation system that uses adaptive digital filters is described. The analysis is based on two well-known models for the NEXT coupling factor, the Bradley and Lin models, and yields the minimum number of adaptive filters required to reduce the NEXT below a prescribed level to within a defined confidence factor. With the minimum number of adaptive filters known, the required computational resources for the application at hand can be estimated. The analysis is further extended to practical situations where the largest NEXT signals chosen for elimination are incorrectly detected, and estimates of the minimum and maximum increase in the uncanceled NEXT due to incorrect detection are then deduced. Simulations show that the estimated minimum number of adaptive filters required and the maximum and minimum increase in uncanceled NEXT due to incorrect detection are fairly close to corresponding estimates obtained on the basis of measurements for both the Bradley and the Lin models. Therefore, by using the proposed analysis the minimum number of adaptive filters can be deduced without the need for time-consuming and expensive experiments.
As the density of data on magnetic disk drives increases, so does the need for more precise position control of the read/write head, especially in the presence of internal and external disturbances. This is achieved b...
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As the density of data on magnetic disk drives increases, so does the need for more precise position control of the read/write head, especially in the presence of internal and external disturbances. This is achieved by measuring the acceleration of the drive and feeding the sensor information forward to the actuator. By matching the electromechanical impedance between the disturbance and the position error, the feedforward controller can cancel the effects of the disturbance. Two techniques are presented for designing the feedforward controller. The first method is an infinite impulse response filter that is designed off-line, and the second is a finite impulse response filter that is adapted on-line using the filtered-x lms algorithm. Both techniques are tested through shake-table experiments, resulting in reductions of the position error signal between 50% and 95%. Copyright (C) 1997 Elsevier Science Ltd.
In this paper, we present a digital background calibration technique for pipelined analog-to-digital converters (ADCs). In this scheme, the capacitor mismatch, residue gain error, and amplifier nonlinearity are measur...
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In this paper, we present a digital background calibration technique for pipelined analog-to-digital converters (ADCs). In this scheme, the capacitor mismatch, residue gain error, and amplifier nonlinearity are measured and then corrected in digital domain. It is based on the error estimation with nonprecision calibration signals in foreground mode, and an adaptive linear prediction structure is used to convert the foreground scheme to the background one. The proposed foreground technique utilizes the lms algorithm to estimate the error coefficients without needing high-accuracy calibration signals. Several simulation results in the context of a 12-b 100-MS/s pipelined ADC are provided to verify the usefulness of the proposed calibration technique. Circuit-level simulation results show that the ADC achieves 28-dB signal-to-noise and distortion ratio and 41-dB spurious-free dynamic range improvement, respectively, compared with the noncalibrated ADC.
The tandem of adaptive filters is common in practice. An example is the tandem of echo cancellers in telecommunication networks. This paper analyzes the convergence characteristics and tracking behavior of two adaptiv...
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The tandem of adaptive filters is common in practice. An example is the tandem of echo cancellers in telecommunication networks. This paper analyzes the convergence characteristics and tracking behavior of two adaptive filters in tandem, together with a comparison of its performance with a single adaptive filter. The adaptive algorithm considered is the lms and the analysis is on mean-square, The coefficient errors correspond to noise, lag bias and lag variance are examined separately, The theoretical results are corroborated with simulations. The study shows that the tandem of two adaptive filters decreases the convergence speed compared to a single adaptive filter. In addition, in steady state and when the step-size is small, tandeming increases the co efficient variance due to noise by a factor of 2.5, the coefficient variance due to tracking lag by a factor of 1.5, but decreases the mean-square coefficient bias due to lag by a factor of 2.
In this paper, several simple and efficient sign based normalized adaptive filters, which are computationally superior having multiplier free weight update loops are used for cancelation of noise in electrocardiograph...
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In this paper, several simple and efficient sign based normalized adaptive filters, which are computationally superior having multiplier free weight update loops are used for cancelation of noise in electrocardiographic (ECG) signals. The proposed implementation is suitable for applications such as biotelemetry, where large signal to noise ratios with less computational complexity are required. These schemes mostly employ simple addition, shift operations and achieve considerable speed up over the other least mean square (lms) based realizations. Simulation studies shows that the proposed realization gives better performance compared to existing realizations in terms of signal to noise ratio and computational complexity. (C) 2010 Elsevier B.V. All rights reserved.
Analog discrete-time finite-impulse-response (FIR) filters have been used as equalizers in digital communication receivers. For high speed applications, an FIR equalizer can be implemented using parallel sample-and-ho...
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Analog discrete-time finite-impulse-response (FIR) filters have been used as equalizers in digital communication receivers. For high speed applications, an FIR equalizer can be implemented using parallel sample-and-holds (S/Hs) and time-interleaved equalizer channels. Mismatches among the parallel S/Hs degrade the equalizer performance. This paper addresses mismatches of DC offsets, gain errors, sample- time errors, and bandwidths in the S/Hs. It is shown that having a different set of adapted coefficients in each equalizer channel can reduce the effects of mismatches. Simulation results are presented for different communication channels.
In this paper, a new family of adaptive filtering algorithms is presented, which aims to combine the small misalignment resulting from the reuse of past weight vectors with the fast convergence arising from the propor...
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In this paper, a new family of adaptive filtering algorithms is presented, which aims to combine the small misalignment resulting from the reuse of past weight vectors with the fast convergence arising from the proportionate adaptation and logarithmic cost functions. This family of algorithms is obtained as a solution to a deterministic constrained optimization problem, by using the Lagrange multipliers technique, which differs from the traditionally employed stochastic gradient technique. Two special cases are proposed, namely the improved mu-law proportionate least mean logarithmic square with reuse of coefficients (IMPLMLS-RC) algorithm and the improved mu-law proportionate least logarithmic absolute difference with reuse of coefficients (IMPLLAD-RC) algorithm. An energy conservation relationship is established, which can be employed to perform stochastic transient analyses of the proposed algorithms. Simulations in system identification and active noise control applications show the advantages of the IMPLMLS-RC and IMPLLAD-RC algorithms over the traditional lms and LAD, and the recently proposed LMLS and LLAD, with respect to both steady-state performance and robustness against impulsive noise.
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