5,583 research outputs found
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.
CMA Channel Equalization Through An Adaptive MMSE Equalizer Based RLS Algorithm
The adaptive algorithm has been widely used in the digital signal processing like channel estimation, channel equalization, echo cancellation, and so on. One of the most important adaptive algorithms is the RLS algorithm. We present in this paper n multiple objective optimization approach to fast blind channel equalization. By investigating first the performance (mean-square error) of the standard fractionally spaced CMA (constant modulus algorithm) equalizer in the presence of noise, we show that CMA local minima exist near the minimum mean-square error (MMSE) equalizers. Consequently, CMA may converge to a local minimum corresponding to a poorly designed MMSE receiver with considerable large mean-square error. The step size in the RLS algorithm decides both the convergence speed and the residual error level, the highest speed of convergence and residual error level
A Novel Approach for Adaptive Signal Processing
Adaptive linear predictors have been used extensively in practice in a wide variety of forms. In the main, their theoretical development is based upon the assumption of stationarity of the signals involved, particularly with respect to the second order statistics. On this basis, the well-known normal equations can be formulated. If high- order statistical stationarity is assumed, then the equivalent normal equations involve high-order signal moments. In either case, the cross moments (second or higher) are needed. This renders the adaptive prediction procedure non-blind. A novel procedure for blind adaptive prediction has been proposed and considerable implementation has been made in our contributions in the past year. The approach is based upon a suitable interpretation of blind equalization methods that satisfy the constant modulus property and offers significant deviations from the standard prediction methods. These blind adaptive algorithms are derived by formulating Lagrange equivalents from mechanisms of constrained optimization. In this report, other new update algorithms are derived from the fundamental concepts of advanced system identification to carry out the proposed blind adaptive prediction. The results of the work can be extended to a number of control-related problems, such as disturbance identification. The basic principles are outlined in this report and differences from other existing methods are discussed. The applications implemented are speech processing, such as coding and synthesis. Simulations are included to verify the novel modelling method
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