Estimation-based synthesis of H∞-optimal adaptive FIR filtersfor filtered-LMS problems

This paper presents a systematic synthesis procedure for H∞-optimal adaptive FIR filters in the context of an active noise cancellation (ANC) problem. An estimation interpretation of the adaptive control problem is introduced first. Based on this interpretation, an H∞ estimation problem is formulated, and its finite horizon prediction (filtering) solution is discussed. The solution minimizes the maximum energy gain from the disturbances to the predicted (filtered) estimation error and serves as the adaptation criterion for the weight vector in the adaptive FIR filter. We refer to this adaptation scheme as estimation-based adaptive filtering (EBAF). We show that the steady-state gain vector in the EBAF algorithm approaches that of the classical (normalized) filtered-X LMS algorithm. The error terms, however, are shown to be different. Thus, these classical algorithms can be considered to be approximations of our algorithm. We examine the performance of the proposed EBAF algorithm (both experimentally and in simulation) in an active noise cancellation problem of a one-dimensional (1-D) acoustic duct for both narrowband and broadband cases. Comparisons to the results from a conventional filtered-LMS (FxLMS) algorithm show faster convergence without compromising steady-state performance and/or robustness of the algorithm to feedback contamination of the reference signal

Calibration of DAC mismatch errors in sigma delta ADCs based on a sine-wave measurement

We present an offline calibration procedure to correct the nonlinearity due element mismatch in the digital-to-analog converter (DAC) of a multibit Sigma Delta-modulation A/D converter. The calibration uses a single measurement on a sinusoidal input signal, from which the DAC errors can be estimated. The main quality of the calibration method is that it can be implemented completely in the digital domain (or in software) and does not intervene in any way in the analog modulator circuit. This way, the technique is a powerful tool for verifying and debugging designs. Due to the simplicity of the method, it may be also a viable approach for factory calibration

Empowering and assisting natural human mobility: The simbiosis walker

This paper presents the complete development of the Simbiosis Smart Walker. The device is equipped with a set of sensor subsystems to acquire user-machine interaction forces and the temporal evolution of user's feet during gait. The authors present an adaptive filtering technique used for the identification and separation of different components found on the human-machine interaction forces. This technique allowed isolating the components related with the navigational commands and developing a Fuzzy logic controller to guide the device. The Smart Walker was clinically validated at the Spinal Cord Injury Hospital of Toledo - Spain, presenting great acceptability by spinal chord injury patients and clinical staf

Improvements in Active Noise Control of Helicopter Noise in Mock Cabin

The application of active noise control (ANC) to interior cabin noise of helicopters is a challenging problem because of multiple tones and significant broadband frequency content. The most common control approach is to use the standard filtered-x algorithm. For this algorithm, the convergence and tracking speed is dependent on the eigenvalues of the filtered-x autocorrelation matrix, with these eigenvalues being frequency dependent. To maintain stability, the system must be implemented based on the slowest converging frequency that will be encountered, which can lead to significant degradation in the overall performance of the control system. This paper will discuss an approach that has been developed which largely overcomes this frequency dependent performance, in a manner that maintains a relatively simple control implementation but significantly improves the overall performance of the control system. The favorable convergence characteristics are demonstrated through the application of helicopter noise in a mock helicopter cabin

A Novel Method for the Application of Adaptive filters for Active Noise Control System

This paper introduces one novel method for active noise control .Though use of filtered-X LMS FIR Adaptive Filter mature in the literature ,this expression illustrates the application of adaptive filters to the attenuation of acoustic noise via active noise control. The reference signal is a noisy version of the undesired sound measured near its source. We shall use a controller filter length of about 44 msec and a step size of 0.0001 for these signal statistics. The resulting algorithm converges after about 5 seconds of adaptation. We also realize adaptive algorithm using IIR filter with active noise to overcome the ability of acoustic feedback . The direct form IIR filter structure, which faces the difficulties of checking stability and of relatively slow convergence speed for noise composed of narrow band components with large power inequality. To overcome these difficulties along with using the direct form IIR filters filtered-u LMS algorithm is used

Adaptive filtering techniques for gravitational wave interferometric data: Removing long-term sinusoidal disturbances and oscillatory transients

It is known by the experience gained from the gravitational wave detector proto-types that the interferometric output signal will be corrupted by a significant amount of non-Gaussian noise, large part of it being essentially composed of long-term sinusoids with slowly varying envelope (such as violin resonances in the suspensions, or main power harmonics) and short-term ringdown noise (which may emanate from servo control systems, electronics in a non-linear state, etc.). Since non-Gaussian noise components make the detection and estimation of the gravitational wave signature more difficult, a denoising algorithm based on adaptive filtering techniques (LMS methods) is proposed to separate and extract them from the stationary and Gaussian background noise. The strength of the method is that it does not require any precise model on the observed data: the signals are distinguished on the basis of their autocorrelation time. We believe that the robustness and simplicity of this method make it useful for data preparation and for the understanding of the first interferometric data. We present the detailed structure of the algorithm and its application to both simulated data and real data from the LIGO 40meter proto-type.Comment: 16 pages, 9 figures, submitted to Phys. Rev.