In signal processing, linear phase is a property of a filter where the phase response of the filter is a linear function of frequency. The result is that all frequency components of the input signal are shifted in time (usually delayed) by the same constant amount (the slope of the linear function), which is referred to as the group delay. Consequently, there is no phase distortion due to the time delay of frequencies relative to one another.
For discrete-time signals, perfect linear phase is easily achieved with a finite impulse response (FIR) filter by having coefficients which are symmetric or anti-symmetric.[1] Approximations can be achieved with infinite impulse response (IIR) designs, which are more computationally efficient. Several techniques are:
A filter is called a linear phase filter if the phase component of the frequency response is a linear function of frequency. For a continuous-time application, the frequency response of the filter is the Fourier transform of the filter's impulse response, and a linear phase version has the form:
H(\omega)=A(\omega) e-j,
where:
\tau
For a discrete-time application, the discrete-time Fourier transform of the linear phase impulse response has the form:
H2\pi(\omega)=A(\omega) e-j,
where:
H2\pi(\omega)
H2\pi(\omega)=\left.\widehatH(z)
| \right| | |
| z=ej |
=\widehatH(ej),
where the
\widehatH
When a sinusoid
, \sin(\omegat+\theta),
\tau,
A(\omega) ⋅ \sin(\omega(t-\tau)+\theta)=A(\omega) ⋅ \sin(\omegat+\theta-\omega\tau),
where:
A(\omega)
\omega\tau
\omega
-\tau
It follows that a complex exponential function:
ei(\omega=\cos(\omegat+\theta)+i ⋅ \sin(\omegat+\theta),
is transformed into:
A(\omega) ⋅ ei(\omega=ei(\omega ⋅ A(\omega)e-i\omega
For approximately linear phase, it is sufficient to have that property only in the passband(s) of the filter, where |A(ω)| has relatively large values. Therefore, both magnitude and phase graphs (Bode plots) are customarily used to examine a filter's linearity. A "linear" phase graph may contain discontinuities of π and/or 2π radians. The smaller ones happen where A(ω) changes sign. Since |A(ω)| cannot be negative, the changes are reflected in the phase plot. The 2π discontinuities happen because of plotting the principal value of
\omega\tau,
In discrete-time applications, one only examines the region of frequencies between 0 and the Nyquist frequency, because of periodicity and symmetry. Depending on the frequency units, the Nyquist frequency may be 0.5, 1.0, π, or ½ of the actual sample-rate. Some examples of linear and non-linear phase are shown below.
A discrete-time filter with linear phase may be achieved by an FIR filter which is either symmetric or anti-symmetric.[3] A necessary but not sufficient condition is:| infty | |
| \sum | |
| n=-infty |
h[n] ⋅ \sin(\omega ⋅ (n-\alpha)+\beta)=0
\alpha,\beta\inR
Systems with generalized linear phase have an additional frequency-independent constant
\beta
H2\pi(\omega)=A(\omega) e-j,
\arg\left[H2\pi(\omega)\right]=\beta-\omegak/2
-\pi<\omega<\pi
Because of this constant, the phase of the system is not a strictly linear function of frequency, but it retains many of the useful properties of linear phase systems.[5]
A(\omega)e-i\omega