Application of the Non-Hermitian Singular Spectrum Analysis to the Exponential Retrieval Problem

Introduction. In practical signal processing and its many applications, researchers and engineers try to find a number of harmonics and their frequencies in a time signal contaminated by noise. In this manuscript we propose a new approach to this problem. Aim. The main goal of this work is to embed the original time series into a set of multi-dimensional information vectors and then use shift-invariance properties of the exponentials. The information vectors are cast into a new basis where the exponentials could be separated from each other. Materials and methods. We derive a stable technique based on the singular value decomposition (SVD) of lagcovariance and cross-covariance matrices consisting of covariance coefficients computed for index translated copies of an original time series. For these matrices a generalized eigenvalue problem is solved. Results. The original time series is mapped into the basis of the generalized eigenvectors and then separated into components. The phase portrait of each component is analyzed by a pattern recognition technique to distinguish between the phase portraits related to exponentials constituting the signal and the noise. A component related to the exponential has a regular structure, its phase portrait resembles a unitary circle/arc. Any commonly used method could be then used to evaluate the frequency associated with the exponential. Conclusion. Efficiency of the proposed and existing methods is compared on the set of examples, including the white Gaussian and auto-regressive model noise. One of the significant benefits of the proposed approach is a way to distinguish false and true frequency estimates by the pattern recognition. Some automatization of the pattern recognition is completed by discarding noise-related components, associated with the eigenvectors that have a modulus less than a certain threshold.Introduction. In practical signal processing and its many applications, researchers and engineers try to find a number of harmonics and their frequencies in a time signal contaminated by noise. In this manuscript we propose a new approach to this problem. Aim. The main goal of this work is to embed the original time series into a set of multi-dimensional information vectors and then use shift-invariance properties of the exponentials. The information vectors are cast into a new basis where the exponentials could be separated from each other. Materials and methods. We derive a stable technique based on the singular value decomposition (SVD) of lagcovariance and cross-covariance matrices consisting of covariance coefficients computed for index translated copies of an original time series. For these matrices a generalized eigenvalue problem is solved. Results. The original time series is mapped into the basis of the generalized eigenvectors and then separated into components. The phase portrait of each component is analyzed by a pattern recognition technique to distinguish between the phase portraits related to exponentials constituting the signal and the noise. A component related to the exponential has a regular structure, its phase portrait resembles a unitary circle/arc. Any commonly used method could be then used to evaluate the frequency associated with the exponential. Conclusion. Efficiency of the proposed and existing methods is compared on the set of examples, including the white Gaussian and auto-regressive model noise. One of the significant benefits of the proposed approach is a way to distinguish false and true frequency estimates by the pattern recognition. Some automatization of the pattern recognition is completed by discarding noise-related components, associated with the eigenvectors that have a modulus less than a certain threshold

Application of the Non-Hermitian Singular Spectrum Analysis to the exponential retrieval problem

We present a new approach to solve the exponential retrieval problem. We derive a stable technique, based on the singular value decomposition (SVD) of lag-covariance and crosscovariance matrices consisting of covariance coefficients computed for index translated copies of an initial time series. For these matrices a generalized eigenvalue problem is solved. The initial signal is mapped into the basis of the generalized eigenvectors and phase portraits are consequently analyzed. Pattern recognition techniques could be applied to distinguish phase portraits related to the exponentials and noise. Each frequency is evaluated by unwrapping phases of the corresponding portrait, detecting potential wrapping events and estimation of the phase slope. Efficiency of the proposed and existing methods is compared on the set of examples, including the white Gaussian and auto-regressive model noise

Using in-situ temperature measurements to estimate saturated soil thermal properties by solving a sequence of optimization problems

International audienceWe describe an approach to find an initial approximation to the thermal properties of soil horizons. This technique approximates thermal conductivity, porosity, unfrozen water content curves in horizons where no direct temperature measurements are available. To determine physical properties of ground material, optimization-based inverse techniques are employed to fit the simulated temperatures to the measured ones. Two major ingredients of these techniques are an algorithm to compute the soil temperature dynamics and a procedure to find an initial approximation to the ground properties. In this article we show how to determine the initial approximation to the physical properties and present a new finite element discretization of the heat equation with phase change to calculate the temperature dynamics in soil. We successfully apply the proposed algorithm to recover the soil properties for the Happy Valley site in Alaska using one-year temperature dynamics. The determined initial approximation is utilized to simulate the temperature dynamics over several consecutive years; the difference between simulated and measured temperatures lies within uncertainties of measurements

Estimation of thermal properties of saturated soils using in-situ temperature measurements

International audienceWe describe an approach to find an initial approximation to the thermal properties of soil horizons. This technique approximates thermal conductivity, porosity, unfrozen water content curve in horizons where no direct temperature measurements are available. To determine physical properties of ground material, optimization-based inverse modeling techniques fitting the simulated and measured temperatures are commonly employed. Two major ingredients of these techniques is an algorithm to compute the soil temperature dynamics and a procedure to find an initial approximation to the ground properties. In this article we show how to determine the initial approximation to the physical properties and present a new finite element discretization of the heat equation with phase change to calculate the temperature dynamics in soil. We successfully applied the proposed algorithm to recover the soil properties for Happy Valley site in Alaska using one-year temperature dynamics. The determined initial approximation was utilized to simulate the temperature dynamics over several consecutive years; the difference between simulated and measured temperatures lies within uncertainties of measurements