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The problem of expanding a finite length signal in a trigonometric series was posed and studied in the late 1700's by renown mathematicians such asBernoulli, d'Alembert, Euler, Lagrange, and Gauss. Indeed, what we now call the Fourier series and the formulas for the coefficients were used by Euler in1780. However, it was the presentation in 1807 and the paper in 1822 by Fourier stating that an arbitrary function could be represented by a series ofsines and cosines that brought the problem to everyone's attention and started serious theoretical investigations and practical applications that continue tothis day . The theoretical work has been at the center of analysis and the practicalapplications have been of major significance in virtually every field of quantitative science and technology. For these reasons and others, the Fourierseries is worth our serious attention in a study of signal processing.
We assume that the signal $x(t)$ to be analyzed is well described by a real or complex valued function of a real variable $t$ defined over a finite interval $\{0\le t\le T\}$ . The trigonometric series expansion of $x(t)$ is given by
where the sines and cosines are the basis functions for the expansion.The energy or power in an electrical, mechanical, etc. system is a function of the square of voltage, current, velocity, pressure, etc. For this reason, thenatural setting for a representation of signals is the Hilbert space of ${L}^{2}[0,T]$ . This modern formulation of the problem is developed in . The sinusoidal basis functions in the trigonometric expansion form a completeorthogonal set in ${L}^{2}[0,T]$ . The orthogonality is easily seen from inner products
It follows that if $x(t)\in {L}^{2}[0,T]$ , then the series converges to $x(t)$ in the sense that $\varepsilon \to 0$ as $N\to \infty $ . The question of point-wise convergence is more difficult. A sufficientcondition that is adequate for most application states: If $f(x)$ is bounded, is piece-wise continuous, and has no more than a finite number of maxima over an interval, the Fourier series converges point-wise to $f(x)$ at all points of continuity and to the arithmetic mean at points of discontinuities. If $f(x)$ is continuous, the series converges uniformly at all points .
A useful condition states that if $x(t)$ and its derivatives through the $q$ th derivative are defined and have bounded variation, the Fourier coefficients $a(k)$ and $b(k)$ asymptotically drop off at least as fast as $\frac{1}{{k}^{q+1}}$ as $k\to \infty $ . This ties global rates of convergence of the coefficients to local smoothnessconditions of the function.
The form of the Fourier series using both sines and cosines makes determination of the peak value or of the location of a particular frequencyterm difficult. A form that explicitly gives the peak value of the sinusoid of that frequency and the location or phase shift of that sinusoid is given by
Although the function to be expanded is defined only over a specific finite region, the series converges to a function that is defined over the real lineand is periodic. It is equal to the original function over the region of definition and is a periodic extension outside of the region. Indeed, onecould artificially extend the given function at the outset and then the expansion would converge everywhere.
It can be very helpful to develop a geometric view of the Fourier series where $x(t)$ is considered to be a vector and the basis functions are the coordinate or basis vectors. The coefficients become the projections of $x(t)$ on the coordinates. The ideas of a measure of distance, size, and orthogonality are important and the definition of error is easy to picture.This is done in using Hilbert space methods.
Note the derivative of a triangle wave is a square wave. Examine the series coefficients to see this. There are many books and web sites on the Fourier series that give insight through examples and demos.
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