Analytical Signal and its Spectral Function

Definition in the Frequency Domain

We consider a real band-pass-like signal $x(t)$ with the corresponding band-pass spectrum $X(f)$ , which has an even real and an odd imaginary part with respect to the frequency zero point. It is assumed that the carrier frequency $f_{\rm T}$ is much larger than the bandwidth of the band-pass signal $x(t)$ .

$\text{Definition:}$ The time function belonging to the physical signal $x(t)$ analytical signal $x_+(t)$ is that time function, whose spectrum fulfills the following property

Analytical Signal in the Frequency Domain

$X_+(f)=\big[1+{\rm sign}(f)\big] \cdot X(f) = \left\{ {2 \cdot X(f) \; \hspace{0.2cm}\rm for\hspace{0.2cm} {\it f} > 0, \atop {\,\,\,\, \rm 0 \; \hspace{0.9cm}\rm for\hspace{0.2cm} {\it f} < 0.} }\right.$

The so called „signum function” is for positive values of $f$ equal to $+1$ and for negative $f$ -values equal to $-1$ .

The (double sided) limit value returns $\sign(0) = 0$ .
The index "+" should make clear that $X_+(f)$ has only parts at positive frequencies.

From the graphic you can see the calculation rule for $X_+(f)$ :

The actual band-pass spectrum $X(f)$ will

doubled at the positive frequencies, and
set to zero at the negative frequencies.

Example of a Spectrum of an Analytical Signal

$\text{Example 1:}$

The graphic

at left shows the (complex) spectrum $X(f)$ of the band-pass signal

$x(t) = 4\hspace{0.05cm}{\rm V} \cdot {\cos} ( 2 \pi f_{\rm u} \hspace{0.03cm}t) + 6\hspace{0.05cm}{\rm V} \cdot {\sin} ( 2 \pi f_{\rm o} \hspace{0.03cm}t).$

and on the right the (complex) spectrum of the analytical signal $x_{+}(t)$ .

Calculation Procedure in The Time Domain

To Derive the Analytical Signal

Now we will take a closer look at the spectrum $X_+(f)$ of the analytical signal and divide it into a with respect to $f = 0$ even part $X_{\rm +g}(f)$ and an odd part $X_{\rm +u}(f)$ :

$X_+(f) = X_{\rm +g}(f) + X_{\rm +u}(f).$

All these spectra are generally complex.

If one considers the nbsp; Mapping Theorem of the Fourier transform, then the following statements are possible on the basis of the graphic:

The even part $X_{\rm +g}(f)$ of $X_{+}(f)$ leads after the Fourier transformation to a real time signal, the odd part $X_{\rm +u}(f)$ to an imaginary one.
It is obvious that $X_{\rm +g}(f)$ is equal to the actual Fourier spectrum $X(f)$ and thus the real part of $x_{\rm +g}(t)$ is equal to the given signal $x(t)$ with band-pass properties.
If we denote the imaginary part with $y(t)$ , the analytical signal is:

$x_+(t)= x(t) + {\rm j} \cdot y(t) .$

According to the generally valid laws of Fourier transform corresponding to the Mapping Theorem , the following applies to the spectral function of the imaginary part:

${\rm j} \cdot Y(f) = X_{\rm +u}(f)= {\rm sign}(f) \cdot X(f) \hspace{0.3cm}\Rightarrow\hspace{0.3cm}Y(f) = \frac{{\rm sign}(f)}{ {\rm j}}\cdot X(f).$

If one transforms this equation into the time domain, the multiplication becomes the convolution, and one gets:

$y(t) = \frac{1}{ {\rm \pi} t} \hspace{0.05cm}\star \hspace{0.05cm}x(t) = \frac{1}{ {\rm \pi}} \cdot \hspace{0.03cm}\int_{-\infty}^{+\infty}\frac{x(\tau)}{ {t - \tau}}\hspace{0.15cm} {\rm d}\tau.$

Representation with Hilbert Transform

At this point it is necessary to briefly discuss a further spectral transformation, which is dealt thoroughly in the book Linear and Time Invariant Systems Systeme .

$\text{Definition:}$ FFor the Hilbert transformed ${\rm H}\left\{x(t)\right\}$ a time function $x(t)$ applies:

$y(t) = {\rm H}\left\{x(t)\right\} = \frac{1}{ {\rm \pi} } \cdot \hspace{0.03cm}\int_{-\infty}^{+\infty}\frac{x(\tau)}{ {t - \tau} }\hspace{0.15cm} {\rm d}\tau.$

This particular integral cannot be solved in a simple, conventional way, but must be evaluated using the principal value of Cauchy .

Correspondingly valid in the frequency domain:

$Y(f) = - {\rm j} \cdot {\rm sign}(f) \cdot X(f) \hspace{0.05cm} .$

The result of the last page can be summarized with this definition as follows:

You get from the real, physical band-pass signal $x(t)$ the analytic signal $x_+(t)$ by adding to $x(t)$ an imaginary part according to the Hilbert transform:

$x_+(t) = x(t)+{\rm j} \cdot {\rm H}\left\{x(t)\right\} .$

The Hilbert transformed $\text{H}\{x(t)\}$ disappears only in the case of $x(t) = \rm const.$ ⇒ DC signal With all other signal forms the analytic signal $x_+(t)$ is therefore always complex.
From the analytical signal $x_+(t)$ the real band-pass signal can be easily determined by real part formation:

$x(t) = {\rm Re}\left\{x_+(t)\right\} .$

{{GraueBox|TEXT= $\text{Example 2:}$ The principle of the Hilbert transformation is illustrated again by the following diagram:

- According to the left representation $\rm (A)$ ,one gets an analytical signal $x_+(t)$ from the physical signal $x(t)$ by adding an imaginary part ${\rm j} \cdot y(t)$ .
Here $y(t) = {\rm H}\left\{x(t)\right\}$ is a real time function, which can be calculated easily in the spectral range by multiplying the spectrum $X(f)$ with $- {\rm j} \cdot \sign(f)$ .

On the Illustration of the Hilbert Transformed

The right representation $\rm (B)$ is equivalent to $\rm (A)$ :

Now applies $x_+(t) = x(t) + z(t)$ with the purely imaginary function $z(t)$ .
A comparison of the two images shows that actually $z(t) = {\rm j} \cdot y(t)$ is valid.}

Vector Diagram Representation of The Harmonic Oscillation

The spectral function $X(f)$ of a harmonic oscillation $x(t) = A \cdot \text{cos}(2\pi f_{\rm T}t - \varphi)$ consists of two Dirac functions at the frequencies

$+f_{\rm T}$ with the complex weight $A/2 \cdot \text{e}^{-\text{j}\hspace{0.05cm}\varphi}$ ,
$-f_{\rm T}$ with the complex weight $A/2 \cdot \text{e}^{+\text{j}\hspace{0.05cm}\varphi}$ .

Thus, the spectrum of the analytical signal is $($ without the Dirac function at the frequency $f =-f_{\rm T})$ :

$X_+(f) = A \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\varphi}\cdot\delta (f - f_{\rm T}) .$

The corresponding time function is obtained by applying the Shifting Theorem:

$x_+(t) = A \cdot {\rm e}^{\hspace{0.05cm} {\rm j}\hspace{0.05cm}\cdot\hspace{0.05cm}( 2 \pi f_{\rm T} t \hspace{0.05cm}-\hspace{0.05cm} \varphi)}.$

This equation describes a rotating pointer with constant angular velocity $\omega_{\rm T} = 2\pi f_{\rm T}$ .

$\text{Example 3:}$ For illustrative reasons the coordinate system in the following figure is rotated to the left (real part up, imaginary part to the left), contrary to the usual representation by $90^\circ$ &nbsp.

Vector Diagram of a Harmonic Oscillation

Anhand dieser Grafik sind folgende Aussagen möglich: On the basis of this diagram the following statements are possible:

At the start time $t = 0$ the pointer of length $A$ (signal amplitude) lies with angle $-\varphi$ in the complex plane. In the drawn example, $\varphi = 45^\circ$ .
For the times $t > 0$ the pointer rotates with constant angular velocity (angular frequency) $\omega_{\rm T}$ in mathematically positive direction, i.e. counterclockwise.
The top of the pointer thus always lies on a circle with radius $A$ and requires exactly the time $T_0$ , i.e. the period of the harmonic oscillation $x(t)$ for one rotation.
The projection of the analytical signal $x_+(t)$ onto the real axis, marked by red dots, provides the instantaneous values of $x(t)$ .

Vector Diagram of a Sum of Harmonic Oscillations

For further description we assume the following spectrum for the analytical signal:

$X_+(f) = \sum_{i=1}^{I}A_i \cdot {\rm e}^{-{\rm j}\hspace{0.05cm}\cdot\hspace{0.05cm} \varphi_i}\cdot\delta (f - f_{i}) .$

The left image shows such a spectrum for the example $I = 3$ . If one chooses $I$ relatively large and the distance between adjacent spectral lines correspondingly small, then (frequency–) continuous spectral functions $X_+(f)$ can also be approximated with the above equation.

Vector Diagram of a Sum of 3 Oscillations

In the right picture the corresponding time function is indicated. This is in general:

$x_+(t) = \sum_{i=1}^{I}A_i \cdot {\rm e}^{ {\rm j}\hspace{0.05cm}\cdot\hspace{0.05cm}(\omega_i \hspace{0.05cm}\cdot\hspace{0.05cm} t \hspace{0.05cm}-\hspace{0.05cm} \varphi_i)}.$

To note about this graphic:

The sketch shows the initial position of the pointers at the start time $t = 0$ corresponding to the amplitudes $A_i$ and the phase positions $\varphi_i$ .
The tip of the resulting pointer compound is marked by the violet cross. One obtains by vectorial addition of the three individual pointers for the time $t = 0$ :

$x_+(t= 0) = \big [1 \cdot \cos(60^\circ) - 1 \cdot {\rm j} \cdot \sin(60^\circ) \big ]+ 2 \cdot \cos(0^\circ)+1 \cdot \cos(180^\circ) = 1.500 - {\rm j} \cdot 0.866.$

For times $t > 0$ the three pointers rotate at different angular speeds $\omega_i = 2\pi f_i$ . The red hand rotates faster than the green hand, but slower than the blue hand.
Since all hands rotate counterclockwise, the resulting hand $x_+(t)$ will also tend to move in this direction. At time $t = 1\,µ\text {s}$ the peak of the resulting pointer for the given parameter values is

$\begin{align*}x_+(t = 1 {\rm \hspace{0.05cm}µ s}) & = 1 \cdot {\rm e}^{-{\rm j}\hspace{0.05cm}\cdot \hspace{0.05cm}60^\circ}\cdot {\rm e}^{{\rm j}\hspace{0.05cm}2 \pi \hspace{0.05cm}\cdot \hspace{0.1cm}40 \hspace{0.05cm} \cdot \hspace{0.1cm} 0.001} + 2\cdot {\rm e}^{{\rm j}\hspace{0.05cm}2 \pi \hspace{0.05cm}\cdot \hspace{0.1cm}50 \hspace{0.05cm} \cdot \hspace{0.1cm} 0.001}- 1\cdot {\rm e}^{{\rm j}\hspace{0.05cm}2 \pi \hspace{0.05cm}\cdot \hspace{0.1cm}60 \hspace{0.05cm} \cdot \hspace{0.1cm} 0.001} = \\ & = 1 \cdot {\rm e}^{-{\rm j}\hspace{0.05cm}\cdot \hspace{0.05cm}45.6^\circ} + 2\cdot {\rm e}^{{\rm j}\hspace{0.05cm}\cdot \hspace{0.05cm}18^\circ}- 1\cdot {\rm e}^{{\rm j}\hspace{0.05cm}\cdot \hspace{0.05cm}21.6^\circ} \approx 1.673- {\rm j} \cdot 0.464.\end{align*}$