Analytic signal

Examples

Example 1

${\displaystyle s(t)=\cos (\omega t),}$ where ${\displaystyle \omega >0.}$

Then:

${\displaystyle \begin{array}{cc}\hat{s}(t)& =\cos \left(\omega t-\frac{\pi }{2}\right)=\sin (\omega t),\\ s_{\mathrm{a}}(t)& =s(t)+j\hat{s}(t)=\cos (\omega t)+j\sin (\omega t)=e^{j\omega t}.\end{array}}$

The last equality is Euler's formula, of which a corollary is $\cos (\omega t)=\frac{1}{2}\left(e^{j\omega t}+e^{j(-\omega )t}\right).$ In general, the analytic representation of a simple sinusoid is obtained by expressing it in terms of complex-exponentials, discarding the negative frequency component, and doubling the positive frequency component. And the analytic representation of a sum of sinusoids is the sum of the analytic representations of the individual sinusoids.

Example 2

Here we use Euler's formula to identify and discard the negative frequency.

${\displaystyle s(t)=\cos (\omega t+\theta )=\frac{1}{2}\left(e^{j(\omega t+\theta )}+e^{-j(\omega t+\theta )}\right)}$

Then:

${\displaystyle s_{\mathrm{a}}(t)=\{\begin{array}{ll}{e}^{j(\omega t+\theta )}=e^{j|\omega |t}\cdot e^{j\theta },& \text{if}\omega >0,\\ e^{-j(\omega t+\theta )}=e^{j|\omega |t}\cdot e^{-j\theta },& \text{if}\omega <0.\end{array}}$

Example 3

This is another example of using the Hilbert transform method to remove negative frequency components. Nothing prevents us from computing ${\displaystyle s_{\mathrm{a}}(t)}$ for a complex-valued ${\displaystyle s(t)}$. But it might not be a reversible representation, because the original spectrum is not symmetrical in general. So except for this example, the general discussion assumes real-valued ${\displaystyle s(t)}$.

${\displaystyle s(t)=e^{-j\omega t}}$, where ${\displaystyle \omega >0}$.

Then:

${\displaystyle \begin{array}{cc}\hat{s}(t)& =j{e}^{-j\omega t},\\ s_{\mathrm{a}}(t)& =e^{-j\omega t}+j^2{e}^{-j\omega t}=e^{-j\omega t}-e^{-j\omega t}=0.\end{array}}$

Properties

Instantaneous amplitude and phase

An analytic signal can also be expressed in polar coordinates:

${\displaystyle s_{\mathrm{a}}(t)=s_{\mathrm{m}}(t)e^{j\varphi (t)},}$

where the following time-variant quantities are introduced:

• ${\displaystyle s_{\mathrm{m}}(t)\triangleq |s_{\mathrm{a}}(t)|}$ is called the instantaneous amplitude or the envelope;
• ${\displaystyle \varphi (t)\triangleq \arg \left[s_{\mathrm{a}}(t)\right]}$ is called the instantaneous phase or phase angle.

In the accompanying diagram, the blue curve depicts ${\displaystyle s(t)}$ and the red curve depicts the corresponding ${\displaystyle s_{\mathrm{m}}(t)}$.

The time derivative of the unwrapped instantaneous phase has units of radians/second, and is called the instantaneous angular frequency:

${\displaystyle \omega (t)\triangleq \frac{d\varphi }{dt}(t).}$

The instantaneous frequency (in hertz) is therefore:

${\displaystyle f(t)\triangleq \frac{1}{2\pi }\omega (t).}$

The instantaneous amplitude, and the instantaneous phase and frequency are in some applications used to measure and detect local features of the signal. Another application of the analytic representation of a signal relates to demodulation of modulated signals. The polar coordinates conveniently separate the effects of amplitude modulation and phase (or frequency) modulation, and effectively demodulates certain kinds of signals.

Complex envelope/baseband

Analytic signals are often shifted in frequency (down-converted) toward 0 Hz, possibly creating [non-symmetrical] negative frequency components: $${\displaystyle {s_{\mathrm{a}}}_{\downarrow }(t)\triangleq s_{\mathrm{a}}(t)e^{-j{\omega }_{0}t}=s_{\mathrm{m}}(t)e^{j(\varphi (t)-{\omega }_{0}t)},}$$ where ${\displaystyle {\omega }_0}$ is an arbitrary reference angular frequency.

This function goes by various names, such as complex envelope and complex baseband. The complex envelope is not unique; it is determined by the choice of ${\displaystyle {\omega }_0}$. This concept is often used when dealing with passband signals. If ${\displaystyle s(t)}$ is a modulated signal, ${\displaystyle {\omega }_0}$ might be equated to its carrier frequency.

In other cases, ${\displaystyle {\omega }_0}$ is selected to be somewhere in the middle of the desired passband. Then a simple low-pass filter with real coefficients can excise the portion of interest. Another motive is to reduce the highest frequency, which reduces the minimum rate for alias-free sampling. A frequency shift does not undermine the mathematical tractability of the complex signal representation. So in that sense, the down-converted signal is still analytic. However, restoring the real-valued representation is no longer a simple matter of just extracting the real component. Up-conversion may be required, and if the signal has been sampled (discrete-time), interpolation (upsampling) might also be necessary to avoid aliasing.

If ${\displaystyle {\omega }_0}$ is chosen larger than the highest frequency of ${\displaystyle s_{\mathrm{a}}(t),}$ then ${\displaystyle {s_{\mathrm{a}}}_{\downarrow }(t)}$ has no positive frequencies. In that case, extracting the real component restores them, but in reverse order; the low-frequency components are now high ones and vice versa. This can be used to demodulate a type of single-sideband signal called lower sideband or inverted sideband.

Other choices of reference frequency are sometimes considered:

• Sometimes ${\displaystyle {\omega }_0}$ is chosen to minimize $${\displaystyle {\int }_0^{+\mathrm{\infty }}(\omega -{\omega }_0{)}^2|S_{\mathrm{a}}(\omega ){|}^{2}d\omega .}$$
• Alternatively, ${\displaystyle {\omega }_0}$ can be chosen to minimize the mean square error in linearly approximating the unwrapped instantaneous phase ${\displaystyle \varphi (t)}$: $${\displaystyle {\int }_{-\mathrm{\infty }}^{+\mathrm{\infty }}[\omega (t)-{\omega }_0{]}^2|s_{\mathrm{a}}(t){|}^{2}dt}$$
• or another alternative (for some optimum ${\displaystyle \theta }$): $${\displaystyle {\int }_{-\mathrm{\infty }}^{+\mathrm{\infty }}[\varphi (t)-({\omega }_{0}t+\theta ){]}^{2}dt.}$$

In the field of time-frequency signal processing, it was shown that the analytic signal was needed in the definition of the Wigner–Ville distribution so that the method can have the desirable properties needed for practical applications.

Sometimes the phrase "complex envelope" is given the simpler meaning of the complex amplitude of a (constant-frequency) phasor; other times the complex envelope ${\displaystyle s_m(t)}$ as defined above is interpreted as a time-dependent generalization of the complex amplitude. Their relationship is not unlike that in the real-valued case: varying envelope generalizing constant amplitude.

Extensions of the analytic signal to signals of multiple variables

The concept of analytic signal is well-defined for signals of a single variable which typically is time. For signals of two or more variables, an analytic signal can be defined in different ways, and two approaches are presented below.

Multi-dimensional analytic signal based on an ad hoc direction

A straightforward generalization of the analytic signal can be done for a multi-dimensional signal once it is established what is meant by negative frequencies for this case. This can be done by introducing a unit vector ${\displaystyle \hat{\mathit{u}}}$ in the Fourier domain and label any frequency vector ${\displaystyle \mathit{\xi }}$ as negative if ${\displaystyle \mathit{\xi }\cdot \hat{\mathit{u}}<0}$. The analytic signal is then produced by removing all negative frequencies and multiply the result by 2, in accordance to the procedure described for the case of one-variable signals. However, there is no particular direction for ${\displaystyle \hat{\mathit{u}}}$ which must be chosen unless there are some additional constraints. Therefore, the choice of ${\displaystyle \hat{\mathit{u}}}$ is ad hoc, or application specific.

The monogenic signal

The real and imaginary parts of the analytic signal correspond to the two elements of the vector-valued monogenic signal, as it is defined for one-variable signals. However, the monogenic signal can be extended to arbitrary number of variables in a straightforward manner, producing an (n + 1)-dimensional vector-valued function for the case of n-variable signals.