FIR transfer function: Difference between revisions

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{{Cleanup rewrite|it is written like a tutorial|FIR transfer function|date=December 2020}}[[Filter (signal processing)#The transfer function|Transfer function filter]] utilizes the transfer function and the [[Convolution theorem]] to produce a filter. In this article, an example of such a filter using finite impulse response is discussed and an application of the filter into real world data is shown.

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== FIR (Finite Impulse Response) Linear filters ==

In digital signal processing, an [[Finite impulse response|FIR filter]] is a time-continuous filter that is invariant with time. This means that the filter does not depend on the specific point of time, but rather depends on the time duration. The specification of this filter uses a [[Linear filter#FIR transfer functions|transfer function]] which hashaving a frequency response whichthat will only pass the desired frequencies of the input. This type of filter is non-recursive, which means that the output can be completely derived at from a combination of the input without any recursive values of the output. This means that there is no feedback loop that feeds the new output the values of previous outputs. This is an advantage over recursive filters such as [[Infinite impulse response|IIR filter (Infinite Impulse Response)]] in the applications that require a linear phase response because it will pass the input without a phase distortion .<ref> IIR Filters and FIR Filters. (2012, June). Retrieved May 04, 2017, from http://zone.ni.com/reference/en-XX/help/370858K-01/genmaths/genmaths/calc_filterfir_iir/</ref>.

== Mathematical model ==

Let the output function be <math>y(t)</math> and the input is <math>x(t)</math>. The convolution of the input with a transfer function <math>h(t)</math> provides a filtered output. The mathematical model of this type of filter is:

: <math>y(t) = \int_{0}^{T} x(t-\tau)\, h(\tau)\, d\tau</math>

h(<math>\tau</math>) is a transfer function of an impulse response to the input. The [[Convolution#Visual explanation|convolution]] allows the filter to only be activated when the input recorded a signal at the same time value. This filter returns the input values (x(t)) if k falls into the support region of function h. This is the reason why this filter is called finite response. If k is outside of the support region, the impulse response is zero which makes output zero. The central idea of this h(<math>\tau</math>) function can be thought of as a quotient of two functions .<ref> Nagai, N. (1990). Linear circuits, systems, and signal processing: Advanced theory and applications. New York: M. Dekker.</ref>.

According to Huang (1981)<ref>Huang, T. S. (1981). Topics in applied physics: Two-Dimensional Digital Signal Processing I (3rd ed., Vol. 42, Topics in Applied Physics). Berlin: Springer.</ref> Using this mathematical ground workmodel, there are four methods of designing non-recursive linear filters with various [[Finite impulse response#Filter design|concurrent filter designs]]:

# [[#Window design method|Window design method]]

# Frequency Sampling method

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:<math>h(t) = \begin{cases} 0, & \forall &-\infty &\le & t &\le 0 \\ e^{-t}, \quad & \forall &0 &\le & t &\le +\infty \end{cases}</math>

Examine this filter in itsThe frequency ___domain,response we see thatof this frequency responsefilter resembles a [Low-pass filter|[low-pass filter]] as in the lower frequency, the magnitude is being passed and the higher frequency, the magnitude of the filter is being deleted which means that the higher frequency is not transferred.

[[File:Single sided filter frequency response.jpg|Single sided filter frequency response]]

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== FIR Transfer function Linear filter Application ==

Linear filter performs better when it is a double-sided filter. This requires the data to be known in advance which makes it a challenge for these filters to function well in situations where signals cannot be known ahead of time such as radio signal processing. However, this means that linear filters are extremely useful in filtering pre-loaded data. In addition, because of its non-recursive nature which preserves the phase angles of the input, linear filters are usually used in [[Image processing|image processing]], [[Video processing|video processing]], data processing or pattern detection. Some examples are image enhancement, restoration and pre-whitening for spectral analysis .<ref>Huang, T. S. (1981). Topics in applied physics: Two-Dimensional Digital Signal Processing I (3rd ed., Vol. 42, Topics in Applied Physics). Berlin: Springer.</ref>. Additionally, linear non-recursive filters are always stable and usually produce a purely real output which makes them more favorable. They are also computationally easy which usually creates a big advantage for using this FIR linear filter.

Figure 6 shows an application of the double-sided filter into filtering flight test data of a UAV. Courtesy of Maddi Stein.

[[File:Maddi.png|Maddi]]

== References ==