Estimation Of Filter Coefficients Research Articles

Adaptive Filtering for Robust Proprioceptive Robot Impact Detection Under Model Uncertainties

In the context of safe human–robot physical interaction, this paper introduces a new method for the detection of dynamic impacts of flexible-joint robot manipulators with their environment. The objective is to detect external impacts applied to the robot using only proprioceptive information with maximal sensitivity. Several model-based detection methods in robotics are based on the difference, called residual, between the estimated and the actual applied torques. Sensitivity of such methods can be limited by model uncertainties that originate either from errors on experimentally identified model parameters, possibly varying with the operating conditions, or the use of simplified models, which results in a residual dependence on the robot’s state. The main contribution of this paper consists of a new adaptive residual evaluation method that takes into account this dependence, which otherwise can lead to a tradeoff between sensitivity and false alarm rate. The proposed approach uses only proprioceptive motor-side measurements and does not require any additional joint position sensors or force/torque sensors. Dynamic effects of a collision on the residual are isolated using bandpass filtering and comparison with a state-dependent dynamic threshold. Adaptive online estimation of filter coefficients avoids the need for extensive experiments for parametric model identification. Experimental evaluation on the CEA backdrivable ASSIST robot arm illustrates the enhancement of the detection sensitivity.

Fast FIR Algorithms for the Continuous Wavelet Transform From Constrained Least Squares

New algorithms for the continuous wavelet transform are developed that are easy to apply, each consisting of a single-pass finite impulse response (FIR) filter, and several times faster than the fastest existing algorithms. The single-pass filter, named WT-FIR-1, is made possible by applying constraint equations to least-squares estimation of filter coefficients, which removes the need for separate low-pass and high-pass filters. Non-dyadic two-scale relations are developed and it is shown that filters based on them can work more efficiently than dyadic ones. Example applications to the Mexican hat wavelet are presented.

Noise‐robust speech analysis using system identification methods

AbstractThis paper proposes a modified linear prediction method for speech analysis, using two system identification methods—the least‐square method and the instrument variable method—for the estimation of the coefficients of an all‐pole filter. Whereas the linear prediction method estimates the coefficients of all‐pole filters from speech signals, which are observed output signals, the system identification method estimates coefficients of all‐pole filters from observed output signals and the input signals. This paper derives a novel technique that estimates input signals from speech signals that are observed output signals with a high degree of accuracy and robustness with respect to added noise, by generating improved prediction error signals. The paper also shows that when voiced speech is to be analyzed, if input signals, which are an impulse chain, can be accurately estimated, the estimation of filter coefficients can yield a high degree of accuracy provided that the least‐square method is used, and that in this manner, the pitch period dependency can be removed. We also show that by applying the instrument variable method using an auxiliary model, the accuracy of estimation of filter coefficients in a noisy environment can be substantially improved while maintaining the properties of the least‐square method. The effectiveness of these system identification methods for speech analysis is demonstrated through computer simulations. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(3): 20–32, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.1137

An analysis of the convergence properties of an active noise control system using augmented error

An active noise control system using the strictly positive real property of the error model in adaptive control theory has been proposed recently. However, this method is of no practical use because it assumes that the error path between the secondary source and the error sensor are known. In this paper, we present the convergence properties of this method without the abovementioned assumption. We make the convergence properties clear by using the modified block diagram and the T-th order IIR filter expression. It is made clear that the disturbance preventing the estimation of filter coefficients varies with its probability. Consequently, this method has a danger of divergence. © 1999 Scripta Technica, Electron Comm Jpn Pt 3, 82(9): 18–28, 1999

An analysis of the convergence properties of an active noise control system using augmented error

An active noise control system using the strictly positive real property of the error model in adaptive control theory has been proposed recently. However, this method is of no practical use because it assumes that the error path between the secondary source and the error sensor are known. In this paper, we present the convergence properties of this method without the abovementioned assumption. We make the convergence properties clear by using the modified block diagram and the T-th order IIR filter expression. It is made clear that the disturbance preventing the estimation of filter coefficients varies with its probability. Consequently, this method has a danger of divergence. © 1999 Scripta Technica, Electron Comm Jpn Pt 3, 82(9): 18–28, 1999

Blind source separation for convolutive mixtures

Blind separation of sources is now a well known problem, and various methods have been proposed for instantaneous mixtures, and convolutive mixtures of narrow-band sources. In this paper, we present adaptive algorithms for separation of wide-band signals, for convolutive mixtures modeled by Finite Impulse Response (FIR) filters. The algorithms estimate coefficients of FIR filters using independence criteria. Under the condition of fourth-order white noise, we prove in this paper that the estimation of filter coefficients may be based on the cancellation of 4th-order output cross-cumulants. However, simpler algorithms only using a few fourth-order cross-cumulants or moments, and 2nd-order moments have the same efficacy. Finally, experimental results point out good performance (about -20 dB of residual crosstalk) even with nonwhite and nonstationary signals like speech signal for simulated mixtures. For recorded mixtures, results are not as good and suggest that the FIR model of mixtures is not realistic enough and must be improved.