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Deconvolution of the Fourier Spectrum Koninklijk Meteorologisch Instituut van Belgi¨e Institut Royal M´et´eorologique de Belgique Deconvolution of the Fourier spectrum F. De Meyer 2003 Wetenschappelijke en Publication scientifique technische publicatie et technique Nr 33 No 33 Uitgegeven door het Edit´epar KONINKLIJK METEOROLOGISCH l’INSTITUT ROYAL INSTITUUT VAN BELGIE METEOROLOGIQUE DE BELGIQUE Ringlaan 3, B -1180 Brussel Avenue Circulaire 3, B -1180 Bruxelles Verantwoordelijke uitgever: Dr. H. Malcorps Editeur responsable: Dr. H. Malcorps Koninklijk Meteorologisch Instituut van Belgi¨e Institut Royal M´et´eorologique de Belgique Deconvolution of the Fourier spectrum F. De Meyer 2003 Wetenschappelijke en Publication scientifique technische publicatie et technique Nr 33 No 33 Uitgegeven door het Edit´epar KONINKLIJK METEOROLOGISCH l’INSTITUT ROYAL INSTITUUT VAN BELGIE METEOROLOGIQUE DE BELGIQUE Ringlaan 3, B -1180 Brussel Avenue Circulaire 3, B -1180 Bruxelles Verantwoordelijke uitgever: Dr. H. Malcorps Editeur responsable: Dr. H. Malcorps 1 Abstract The problem of estimating the frequency spectrum of a real continuous function, which is measured only at a finite number of discrete times, is discussed in this tutorial review. Based on the deconvolu- tion method, the complex version of the one-dimensional CLEAN algorithm provides a simple way to remove the artefacts introduced by the sampling and the effects of missing data from the computed Fourier spectrum. The technique is very appropriate in the case of equally spaced data, as well as for data samples randomly distributed in time. An example of the application of CLEAN to a synthetic spectrum of a small number of harmonic components at discrete frequencies is shown. The case of a low signal-to-noise time sequence is also presented by illustrating how CLEAN recovers the spectrum of the declination component of the geomagnetic field, measured in the magnetic observatory of Dourbes. 1. Introduction Synthesis maps in radio astronomy are generated by Fourier transformation of interferome- ter visibility data to produce a map of the sky (Thompson et al., 1986). Because only a finite number of visibility points are gathered, the problems of sampling apply to interferometer maps. The CLEAN algorithm is widely used in two-dimensional image reconstruction and performs an approximate deconvolution of the ‘true map’ of the sky from the ‘dirty map’ procreated from the data, in essence, removing the false features introduced inherently by the finite sampling. By adapting the two-dimensional CLEAN procedure developed for use in aperture synthesis, Roberts et al. (1987) discussed a rather intuitive way of handling the difficulties associated with the spurious apparent responses that arise from the incompleteness of the discrete sampling of a continuous time function. A multitude of spectral estimation algorithms for discrete time series have been proposed (Kay and Marple, 1981). Comparisons among various competing techniques have been based on limited computer simulations, which can be misleading. Here we select the conventional Fourier spectral estimator because the effects of finite, discrete sampling on the performance of the classical periodogram are precisely known and can be almost completely rectified. Estimation of the power spectral density, or simply the spectrum, of discretely sampled deterministic and stochastic processes is usually based on procedures employing the Fast Fourier Transform or FFT (Brigham, 1974), which implicitly assumes that the data are periodic outside the observ- ing interval. Unfortunately the FFT technique is directly applicable only when the input data sequence is evenly spaced in time. Often observations cannot be controlled to the extent that observability constraints may cause an unequally spaced sample domain. One is then forced to extract spectral information from these irregularly sampled data. The FFT approach to spectrum analysis is computationally efficient and produces reason- able outcomes for a large class of signal processes (Briggs and Henson, 1995). In spite of these advantages, there are several inherent performance limitations of the FFT procedure. The most prominent shortcoming is that of frequency resolution, i.e., the ability to distinguish the spec- tral responses of distinct signals. The frequency resolution is roughly the reciprocal of the time interval over which sampled data are available. A second imperfection is due to the implicit windowing of the data that occurs when processing with the FFT. Windowing manifests itself as ‘leakage’ in the spectral domain, that is, energy in the main lobe of a spectral response ‘leaks’ into the sidelobes, obscuring and distorting other spectral responses that are present. In fact, weak signal spectral responses can be masked by higher sidelobes from stronger spectral com- ponents. These two performance limitations of the FFT approach are particularly troublesome when analyzing short data records. 2 In an attempt to alleviate the inherent shortcomings of the FFT approach, many alternative spectral estimation procedures have been proposed. For instance, Kay and Marple (1981) discuss the improvement that may result from non-traditional methods such as the autoregressive (AR) method or the maximum entropy method (MEM). Claims have been made concerning the de- gree of improvement obtained in the spectral resolution and the signal detectability when these numerical techniques are applied to numerical data. These performance advantages, though, strongly depend upon the signal-to-noise ratio (SNR) of the input data, as might be expected. In fact, for low enough SNR’s the ‘modern’ spectral estimators are often no better than those obtained with conventional FFT processing. Even in those cases where improved spectral fidelity is achieved by use of a different spectral estimation procedure, the computational requirements of that alternate method may be substantially higher than FFT computation. This may make some spectral estimators unattractive for real-time implementation. Schwarz (1978) has shown that CLEAN is a statistically correct method of least squares fit- ting sinusoidal functions to the observations which is the conventional framework of the discrete Fourier transform. Fourier inversion of a finite representation of a continuous function leads to a frequency spectrum distorted by (1) the limited frequency resolution due to the finite time span of the data sample, and (2) spurious apparent responses that are caused by the incompleteness of the sampling. There is an intuitive way of handling the difficulties associated with the latter effect. With a view to practical applications heuristic arguments are used in this discussion to make the discourse more easily accessible. The CLEAN algorithm performs, in essence, a non-linear deconvolution of the Fourier spectrum on the frequency axis, equivalent to a least squares interpolation in the time domain. The numerical method is particularly suitable for functions whose spectra are dominated by a few harmonic components at discrete frequencies. The CLEAN technique for time series spectral analysis (Roberts et al., 1987) works by subtracting from the noisy or ‘dirty’ spectrum, which is the convolution of the ‘true’ spectrum with the ‘dirty beam’ generated by the finite number of data, the response expected from a sinusoidal component with frequency corresponding to the maximum of the spectrum. In this way a residual spectrum is produced from which both the component and the spurious features due to sampling have been removed. This procedure of ‘CLEANing’ the spectrum is repeated on successive residual spectra until nothing is left but noise. The set of resulting ‘clean com- ponents’ is then used as a model for the time function. This model is convolved with a ‘clean beam’ which has the same resolution as the original ‘spectral window’ but no sidelobes. The convolution of the clean components model with the clean beam serves to weight down the interpolated information at time instants outside those sampled. Finally, to preserve the noise level of the spectrum and to incorporate any components which were not resolved by CLEAN, the final residual spectrum is added to the convolved clean components to produce the ‘clean spectrum’. The one-dimensional version of the CLEAN deconvolution technique is especially useful for spectral analysis of unequally spaced observations and provides a simple way to dispose of the artefacts of missing data, a situation which is frequently encountered in practical time series. As a first example, the application of CLEAN will be illustrated by means of the frequency analysis of a synthetic spectrum, that is generated by a small number of harmonic components at discrete frequencies. The method is also used in the search for periodic variability in the time series of the hourly values of the magnetic declination, measured in the observatory of Dourbes, Belgium, which is characterized by a spectrum having a low signal-to-noise ratio. 3 2. The Fourier spectrum of a continuous function The basic recourse for the frequency analysis of an aperiodic or transient function is the Fourier integral (Bracewell, 1986). Here we follow the notation of Jenkins and Watts (1968). The spec- trum of an analog function x(t), known for all time t, is given by the continuous Fourier transform (denoted by the operator F)ofx(t), ∞ X(f) ≡F{x(t)} = x(t) e−2πift dt, −∞≤f ≤∞, (1) −∞ which defines the contribution of each frequency f to x(t). The inverse Fourier transform (de- noted by F −1) is defined by the Riemann
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