[Paper Review] Adaptive Identification of VIRGO-like Noise Spectrum

E. Cuoco, Giuseppe Curci+1 more|ArXiv.org|Sep 16, 1997

Structural Health Monitoring Techniques2 references3 citations

TL;DR

This paper proposes an adaptive online whitening filter for VIRGO-like gravitational wave detector noise by modeling the noise spectrum as an autoregressive process after pre-filtering to flatten low-frequency components. Using gradient-based and least-squares lattice adaptive algorithms, the method successfully identifies and tracks the noise spectrum in real time, demonstrating feasibility for real-time noise mitigation in gravitational wave data analysis.

ABSTRACT

The aim of this work is to show how it is possible to build an on line whitening filter in an adaptive way. We have modeled the VIRGO noise spectrum as an autoregressive stochastic process, after a pre-filtering of the theoretical curve which flattens the low frequency part of the spectrum. We have tested some very popular adaptive algorithms, based on the gradient methods and on the least squares methods with a lattice structure filter.

Motivation & Objective

• To develop an on-line adaptive whitening filter for VIRGO-type gravitational wave detectors.
• To model the complex VIRGO noise spectrum as an autoregressive stochastic process after spectral pre-processing.
• To evaluate the performance of gradient-based and least-squares lattice adaptive algorithms in tracking the noise characteristics.
• To enable real-time noise spectrum identification for improved data quality in gravitational wave signal processing.
• To support robust detection of weak gravitational wave signals by dynamically compensating for non-stationary noise.

Proposed method

• The noise power spectral density is pre-filtered to flatten the low-frequency region, simplifying the modeling task.
• The pre-processed spectrum is modeled as an autoregressive (AR) stochastic process to represent the noise dynamics.
• Adaptive algorithms based on gradient descent and recursive least squares with lattice structure are applied to identify the AR parameters in real time.
• The lattice structure enables stable and efficient recursive estimation of the AR coefficients.
• The adaptive filter continuously updates its parameters to track changes in the noise spectrum.
• The method is tested using simulated VIRGO-like noise data to evaluate convergence and tracking performance.

Experimental results

Research questions

• RQ1 Can an adaptive filter accurately identify and track the time-varying characteristics of VIRGO-like noise spectra in real time?
• RQ2 How effective are gradient-based and least-squares lattice algorithms in estimating the AR parameters of a pre-processed noise spectrum?
• RQ3 Does pre-filtering the theoretical noise curve to flatten low-frequency components improve the convergence and stability of adaptive identification?
• RQ4 Can the adaptive whitening filter be implemented online with sufficient speed and accuracy for real-time gravitational wave data processing?
• RQ5 What is the performance trade-off between computational complexity and estimation accuracy in different adaptive algorithm variants?

Key findings

• The adaptive identification method successfully tracks the VIRGO-like noise spectrum in real time using both gradient and least-squares lattice algorithms.
• The pre-filtering step significantly improves the convergence and stability of the adaptive algorithms by reducing low-frequency dynamics.
• The least-squares lattice algorithm demonstrated superior performance in terms of convergence speed and numerical stability.
• The method enables the construction of an on-line whitening filter that adapts to non-stationary noise conditions.
• The results confirm the feasibility of using adaptive AR modeling for real-time noise mitigation in gravitational wave detectors.
• The approach is suitable for integration into online data analysis pipelines for future gravitational wave observatories.