Finite impulse response
Encyclopedia
A finite impulse response (FIR) filter is a type of a signal processing filter
whose impulse response
(or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response
(IIR) filters, which have internal feedback and may continue to respond indefinitely (usually decaying). The impulse response
of an Nth-order discrete-time FIR filter (i.e. with a Kronecker delta impulse input) lasts for N+1 samples, and then dies to zero.
FIR filters can be discrete-time or continuous-time, and digital
or analog.
its input signal x with its impulse response
b.
For a discrete-time FIR filter, the output is a weighted sum of the current and a finite number of previous values of the input. The operation is described by the following equation, which defines the output sequence y[n] in terms of its input sequence x[n]:
where:
(IIR) filter. FIR filters:
The main disadvantage of FIR filters is that considerably more computation power in a general purpose processor is required compared to an IIR filter with similar sharpness or selectivity, especially when low frequency (relative to the sample rate) cutoffs are needed. However many digital signal processors provide specialized hardware features to make FIR filters approximately as efficient as IIR for many applications.
can be calculated if we set 
in the above relation, where 
is the Kronecker delta impulse.
The impulse response for an FIR filter then becomes the set of coefficients
, as follows
for
to 
.
The Z-transform
of the impulse response yields the transfer function
of the FIR filter
FIR filters are clearly bounded-input bounded-output (BIBO) stable
, since the output is a sum of a finite number of finite multiples of the input values, so can be no greater than
times the largest value appearing in the input.
Software packages like MATLAB
, GNU Octave
, Scilab
, and SciPy
provide convenient ways to apply these different methods.
Some filter specifications refer to the time-domain shape of the input signal the filter is expected to "recognize". The optimum matched filter
for separating any waveform from white noise is obtained by sampling that shape and using those samples in reverse order as the coefficients of the filter -- giving the filter an impulse response that is the time-reverse of the expected input signal.
to it – in the time domain, multiplying the infinite impulse by the window function. This results in the frequency response of the IIR being convolved with the frequency response of the window function – thus the imperfections of the FIR filter (compared to the ideal IIR filter) can be understood in terms of the frequency response of the window function.
The ideal frequency response of a window is a Dirac delta function
, as that results in the frequency response of the FIR filter being identical to that of the IIR filter, but this is not attainable for finite windows, and deviations from this yield differences between the FIR response and the IIR response.
filter, especially when followed by decimation. The filter coefficients are found via the following equation:
for 
To provide a more specific example, we select the filter order:
The impulse response of the resulting filter is:
The Fig. (a) on the right shows the block diagram of a 2nd-order moving-average filter discussed below. To discuss stability and spectral topics we take the z-transform
of the impulse response:
Fig. (b) on the right shows the pole-zero diagram of the filter. Zero frequency (DC) corresponds to (1,0), positive frequencies advancing counterclockwise around the circle to (-1,0) at half the sample frequency.
Two poles are located at the origin, and two zeros are located at
, 
The frequency response, for frequency ω in radians per sample, is:
Fig. (c) on the right shows the magnitude and phase plots of the frequency response. Clearly, the moving-average filter passes low frequencies with a gain near 1, and attenuates high frequencies. This is a typical low-pass filter characteristic. Frequencies above π are aliases
of the frequencies below π, and are generally ignored or filtered out if reconstructing a continuous-time signal. The following figure shows the phase response
. Since the phase always follows a straight line except where it has been reduced modulo π radians (should be 2π), the linear phase property is demonstrated.
Filter (signal processing)
In signal processing, a filter is a device or process that removes from a signal some unwanted component or feature. Filtering is a class of signal processing, the defining feature of filters being the complete or partial suppression of some aspect of the signal...
whose impulse response
Impulse response
In signal processing, the impulse response, or impulse response function , of a dynamic system is its output when presented with a brief input signal, called an impulse. More generally, an impulse response refers to the reaction of any dynamic system in response to some external change...
(or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response
Infinite impulse response
Infinite impulse response is a property of signal processing systems. Systems with this property are known as IIR systems or, when dealing with filter systems, as IIR filters. IIR systems have an impulse response function that is non-zero over an infinite length of time...
(IIR) filters, which have internal feedback and may continue to respond indefinitely (usually decaying). The impulse response
Impulse response
In signal processing, the impulse response, or impulse response function , of a dynamic system is its output when presented with a brief input signal, called an impulse. More generally, an impulse response refers to the reaction of any dynamic system in response to some external change...
of an Nth-order discrete-time FIR filter (i.e. with a Kronecker delta impulse input) lasts for N+1 samples, and then dies to zero.
FIR filters can be discrete-time or continuous-time, and digital
Digital
A digital system is a data technology that uses discrete values. By contrast, non-digital systems use a continuous range of values to represent information...
or analog.
Definition
The output y of a linear time invariant system is determined by convolvingConvolution
In mathematics and, in particular, functional analysis, convolution is a mathematical operation on two functions f and g, producing a third function that is typically viewed as a modified version of one of the original functions. Convolution is similar to cross-correlation...
its input signal x with its impulse response
Impulse response
In signal processing, the impulse response, or impulse response function , of a dynamic system is its output when presented with a brief input signal, called an impulse. More generally, an impulse response refers to the reaction of any dynamic system in response to some external change...
b.
For a discrete-time FIR filter, the output is a weighted sum of the current and a finite number of previous values of the input. The operation is described by the following equation, which defines the output sequence y[n] in terms of its input sequence x[n]:
where:
is the input signal,
is the output signal,
are the filter coefficients, also known as tap weights, that make up the impulse response,
is the filter order; an 
th-order filter has 
terms on the right-hand side. The 
in these terms are commonly referred to as s, based on the structure of a tapped delay lineDigital delay lineA digital delay line is a discrete element in digital filter theory, which allows a signal to be delayed by a number of samples. If the delay is an integer multiple of samples digital delay lines are often implemented as circular buffers...
that in many implementations or block diagrams provides the delayed inputs to the multiplication operations. One may speak of a "5th order/6-tap filter", for instance.
Properties
An FIR filter has a number of useful properties which sometimes make it preferable to an infinite impulse responseInfinite impulse response
Infinite impulse response is a property of signal processing systems. Systems with this property are known as IIR systems or, when dealing with filter systems, as IIR filters. IIR systems have an impulse response function that is non-zero over an infinite length of time...
(IIR) filter. FIR filters:
- Require no feedback. This means that any rounding errors are not compounded by summed iterations. The same relative error occurs in each calculation. This also makes implementation simpler.
- Are inherently stable. This is due to the fact that, because there is no required feedback, all the poles are located at the origin and thus are located within the unit circle (the required condition for stability in a Z transformed system).
- They can easily be designed to be linear phaseLinear phaseLinear phase is a property of a filter, where the phase response of the filter is a linear function of frequency, excluding the possibility of wraps at \pm\pi. In a causal system, perfect linear phase can be achieved with a discrete-time FIR filter...
by making the coefficient sequence symmetric; linear phase, or phase change proportional to frequency, corresponds to equal delay at all frequencies. This property is sometimes desired for phase-sensitive applications, for example data communications, crossover filtersAudio crossoverAudio crossovers are a class of electronic filter used in audio applications. Most individual loudspeaker drivers are incapable of covering the entire audio spectrum from low frequencies to high frequencies with acceptable relative volume and lack of distortion so most hi-fi speaker systems use a...
, and masteringAudio masteringMastering, a form of audio post-production, is the process of preparing and transferring recorded audio from a source containing the final mix to a data storage device ; the source from which all copies will be produced...
.
The main disadvantage of FIR filters is that considerably more computation power in a general purpose processor is required compared to an IIR filter with similar sharpness or selectivity, especially when low frequency (relative to the sample rate) cutoffs are needed. However many digital signal processors provide specialized hardware features to make FIR filters approximately as efficient as IIR for many applications.
Impulse response
The impulse responseImpulse response
In signal processing, the impulse response, or impulse response function , of a dynamic system is its output when presented with a brief input signal, called an impulse. More generally, an impulse response refers to the reaction of any dynamic system in response to some external change...
can be calculated if we set in the above relation, where is the Kronecker delta impulse.The impulse response for an FIR filter then becomes the set of coefficients
, as followsfor
to .The Z-transform
Z-transform
In mathematics and signal processing, the Z-transform converts a discrete time-domain signal, which is a sequence of real or complex numbers, into a complex frequency-domain representation....
of the impulse response yields the transfer function
Transfer function
A transfer function is a mathematical representation, in terms of spatial or temporal frequency, of the relation between the input and output of a linear time-invariant system. With optical imaging devices, for example, it is the Fourier transform of the point spread function i.e...
of the FIR filter
FIR filters are clearly bounded-input bounded-output (BIBO) stable
BIBO stability
In electrical engineering, specifically signal processing and control theory, BIBO stability is a form of stability for linear signals and systems that take inputs. BIBO stands for Bounded-Input Bounded-Output...
, since the output is a sum of a finite number of finite multiples of the input values, so can be no greater than
times the largest value appearing in the input.Filter design
To design a filter means to select the coefficients such that the system has specific characteristics. The required characteristics are stated in filter specifications. Most of the time filter specifications refer to the frequency response of the filter. There are different methods to find the coefficients from frequency specifications:- Window design method
- Frequency Sampling method
- Weighted least squares design
- Parks-McClellan method (also known as the Equiripple, Optimal, or Minimax method). The Remez exchange algorithmRemez algorithmThe Remez algorithm , published by Evgeny Yakovlevich Remez in 1934 is an iterative algorithm used to find simple approximations to functions, specifically, approximations by functions in a Chebyshev space that are the best in the uniform norm L∞ sense.A typical...
is commonly used to find an optimal equiripple set of coefficients. Here the user specifies a desired frequency response, a weighting function for errors from this response, and a filter order N. The algorithm then finds the set of
coefficients that minimize the maximum deviation from the ideal. Intuitively, this finds the filter that is as close as you can get to the desired response given that you can use only 
coefficients. This method is particularly easy in practice since at least one text includes a program that takes the desired filter and N, and returns the optimum coefficients. - Equiripple FIR filters can be designed using the FFT algorithms as well. The algorithm is iterative in nature. You simply compute the DFT of an initial filter design that you have using the FFT algorithm (if you don't have an initial estimate you can start with h[n]=delta[n]). In the Fourier domain or FFT domain you correct the frequency response according to your desired specs and compute the inverse FFT. In time-domain you retain only N of the coefficients (force the other coefficients to zero). Compute the FFT once again. Correct the frequency response according to specs.
Software packages like MATLAB
MATLAB
MATLAB is a numerical computing environment and fourth-generation programming language. Developed by MathWorks, MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages,...
, GNU Octave
GNU Octave
GNU Octave is a high-level language, primarily intended for numerical computations. It provides a convenient command-line interface for solving linear and nonlinear problems numerically, and for performing other numerical experiments using a language that is mostly compatible with MATLAB...
, Scilab
Scilab
Scilab is an open source, cross-platform numerical computational package and a high-level, numerically oriented programming language. Itcan be used for signal processing, statistical analysis, image enhancement, fluid dynamics simulations, numerical optimization, and modeling and simulation of...
, and SciPy
SciPy
SciPy is an open source library of algorithms and mathematical tools for the Python programming language.SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and...
provide convenient ways to apply these different methods.
Some filter specifications refer to the time-domain shape of the input signal the filter is expected to "recognize". The optimum matched filter
Matched filter
In telecommunications, a matched filter is obtained by correlating a known signal, or template, with an unknown signal to detect the presence of the template in the unknown signal. This is equivalent to convolving the unknown signal with a conjugated time-reversed version of the template...
for separating any waveform from white noise is obtained by sampling that shape and using those samples in reverse order as the coefficients of the filter -- giving the filter an impulse response that is the time-reverse of the expected input signal.
Window design method
In the Window Design Method, one designs an ideal IIR filter, then applies a window functionWindow function
In signal processing, a window function is a mathematical function that is zero-valued outside of some chosen interval. For instance, a function that is constant inside the interval and zero elsewhere is called a rectangular window, which describes the shape of its graphical representation...
to it – in the time domain, multiplying the infinite impulse by the window function. This results in the frequency response of the IIR being convolved with the frequency response of the window function – thus the imperfections of the FIR filter (compared to the ideal IIR filter) can be understood in terms of the frequency response of the window function.
The ideal frequency response of a window is a Dirac delta function
Dirac delta function
The Dirac delta function, or δ function, is a generalized function depending on a real parameter such that it is zero for all values of the parameter except when the parameter is zero, and its integral over the parameter from −∞ to ∞ is equal to one. It was introduced by theoretical...
, as that results in the frequency response of the FIR filter being identical to that of the IIR filter, but this is not attainable for finite windows, and deviations from this yield differences between the FIR response and the IIR response.
Moving average example
A moving average filter is a very simple FIR filter. It is sometimes called a boxcarBoxcar
A boxcar is a railroad car that is enclosed and generally used to carry general freight. The boxcar, while not the simplest freight car design, is probably the most versatile, since it can carry most loads...
filter, especially when followed by decimation. The filter coefficients are found via the following equation:
for To provide a more specific example, we select the filter order:
The impulse response of the resulting filter is:
The Fig. (a) on the right shows the block diagram of a 2nd-order moving-average filter discussed below. To discuss stability and spectral topics we take the z-transform
Z-transform
In mathematics and signal processing, the Z-transform converts a discrete time-domain signal, which is a sequence of real or complex numbers, into a complex frequency-domain representation....
of the impulse response:
Fig. (b) on the right shows the pole-zero diagram of the filter. Zero frequency (DC) corresponds to (1,0), positive frequencies advancing counterclockwise around the circle to (-1,0) at half the sample frequency.
Two poles are located at the origin, and two zeros are located at
, The frequency response, for frequency ω in radians per sample, is:
Fig. (c) on the right shows the magnitude and phase plots of the frequency response. Clearly, the moving-average filter passes low frequencies with a gain near 1, and attenuates high frequencies. This is a typical low-pass filter characteristic. Frequencies above π are aliases
Aliasing
In signal processing and related disciplines, aliasing refers to an effect that causes different signals to become indistinguishable when sampled...
of the frequencies below π, and are generally ignored or filtered out if reconstructing a continuous-time signal. The following figure shows the phase response
Phase response
In signal processing and electrical engineering, phase response is the relationship between the phase of a sinusoidal input and the output signal passing through any device that accepts input and produces an output signal, such as an amplifier or a filter....
. Since the phase always follows a straight line except where it has been reduced modulo π radians (should be 2π), the linear phase property is demonstrated.
See also
- Electronic filterElectronic filterElectronic filters are electronic circuits which perform signal processing functions, specifically to remove unwanted frequency components from the signal, to enhance wanted ones, or both...
- Filter (signal processing)Filter (signal processing)In signal processing, a filter is a device or process that removes from a signal some unwanted component or feature. Filtering is a class of signal processing, the defining feature of filters being the complete or partial suppression of some aspect of the signal...
- Infinite impulse response (IIR) filterInfinite impulse responseInfinite impulse response is a property of signal processing systems. Systems with this property are known as IIR systems or, when dealing with filter systems, as IIR filters. IIR systems have an impulse response function that is non-zero over an infinite length of time...
- Z-transformZ-transformIn mathematics and signal processing, the Z-transform converts a discrete time-domain signal, which is a sequence of real or complex numbers, into a complex frequency-domain representation....
(specifically Linear constant-coefficient difference equation) - Filter designFilter designFilter design is the process of designing a filter , often a linear shift-invariant filter, that satisfies a set of requirements, some of which are contradictory...
- Cascaded Integrator-Comb FilterCascaded Integrator-Comb FilterIn digital signal processing, a cascaded integrator-comb is an optimized class of finite impulse response filter combined with an interpolator or decimator.A CIC filter consists of one or more integrator and comb filter pairs...
- Compact support
External links
- Notes on the Optimal Design of FIR Filters ConnexionsConnexionsConnexions is a global repository of educational content provided by Rice University. The entire collection is available free of charge, and students and learners alike can explore all the content they desire....
online book by John Treichler (2008). - FIR FAQ provided by dspguru.com.
- BruteFIR; Software for applying long FIR filters to multi-channel digital audio, either offline or in realtime.
- Freeverb3 Reverb Impulse Response Processor
- FIR filter design
- An n-tap FIR filter written in VHDL
- Worked examples and explanation for designing FIR filters using windowing. Includes code examples.
- A JAVA applet with different FIR-filters; the filters are applied to sound and the results can be heard immediately. The source code is also available.
- Matlab code; Matlab code for "Equiripple FIR filter design by the FFT algorithm" by A. Enis Cetin, O. N. Gerek and Y. Yardimci, IEEE Signal Processing Magazine, 1997.