6,744 research outputs found
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
A technique for improved stability of adaptive feedforward controllers without detailed uncertainty measurements
Model errors in adaptive controllers for reduction of broadband noise and vibrations may lead to unstable systems or increased error signals. Previous work has shown that the addition of a low-authority controller that increases damping in the system may lead to improved performance of an adaptive, high-authority controller. Other researchers have suggested to use frequency dependent regularization based on measured uncertainties. In this paper an alternative method is presented that avoids the disadvantages of these methods namely the additional complex hardware, and the need to obtain detailed information of the uncertainties. An analysis is made of an active noise control system in which a difference exists between the secondary path and the model as used in the controller. The real parts of the eigenvalues that determine the stability of the system are expressed in terms of the amount of uncertainty and the singular values of the secondary path. Based on these expressions, modifications of the feedforward control scheme are suggested that aim to improved performance without requiring detailed uncertainty measurements. For an active noise control system in a room it is shown that the technique leads to improved performance in terms of robustness and the amount of reduction of the error signals
Active Noise Control with Sampled-Data Filtered-x Adaptive Algorithm
Analysis and design of filtered-x adaptive algorithms are conventionally done
by assuming that the transfer function in the secondary path is a discrete-time
system. However, in real systems such as active noise control, the secondary
path is a continuous-time system. Therefore, such a system should be analyzed
and designed as a hybrid system including discrete- and continuous- time
systems and AD/DA devices. In this article, we propose a hybrid design taking
account of continuous-time behavior of the secondary path via lifting
(continuous-time polyphase decomposition) technique in sampled-data control
theory
On feed-through terms in the lms algorithm
The well known least mean squares (LMS) algorithm is studied as a control system. When
applied in a noise canceller a block diagram approach is used to show that the step size has
two upper limits. One is the conventional limit beyond which instability results. The
second limit shows that if the step size is chosen to be too large then feed-through terms
consisting of signal times noise will result in an additive term at the noise canceller output.
This second limit is smaller than the first and will cause distortion at the noise canceller
output
Accounting for outliers and calendar effects in surrogate simulations of stock return sequences
Surrogate Data Analysis (SDA) is a statistical hypothesis testing framework
for the determination of weak chaos in time series dynamics. Existing SDA
procedures do not account properly for the rich structures observed in stock
return sequences, attributed to the presence of heteroscedasticity, seasonal
effects and outliers. In this paper we suggest a modification of the SDA
framework, based on the robust estimation of location and scale parameters of
mean-stationary time series and a probabilistic framework which deals with
outliers. A demonstration on the NASDAQ Composite index daily returns shows
that the proposed approach produces surrogates that faithfully reproduce the
structure of the original series while being manifestations of linear-random
dynamics.Comment: 21 pages, 7 figure
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.
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Real-time adaptive filtering of dental drill noise using a digital signal processor
The application of noise reduction methods requires the integration of acoustics engineering and digital signal processing, which is well served by a mechatronic approach as described in this paper. The Normalised Least Mean Square (NLMS) algorithm is implemented on the Texas Instruments TMS320C6713 DSK Digital Signal Processor (DSP) as an adaptive digital filter for dental drill noise. Blocks within the Matlab/Simulink Signal Processing Blockset and the Embedded Target for TI C6000 DSP family are used. A working model of the algorithm is then transferred to the Code Composer Studio (CCS), where the desired code can be linked and transferred to the target DSP. The experimental rig comprises a noise reference microphone, a microphone for the desired signal, the DSK and loudspeakers. Different load situations of the dental drill are considered as the noise characteristics change when the drill load changes. The result is that annoying drill noise peaks, which occur in a frequency range from 1.5 kHz to 10 kHz, are filtered out adaptively by the DSP. Additionally a schematic design for its implementation in a dentist’s surgery will also be presented
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