IIR Filters

Infinite Impulse Response (IIR) filters are such filters which include recursion, respectively feedback, in their implementation. Their impulse response does thus not drop to zero after a given time. They can become instable due to the feedback. However, IIR filters require less coefficients and thus less operations to get a filter with the desired effect than FIR filters. As FIR filters, IIR filters create an output sequence $y[n]$ from an input sequence $x[n]$:

The Difference Equation

For the difference equation the recursion is included with additional coefficients $a_n$, feeding back the output signal $y[n]$ with different delays. The following difference function represents a second order IIR filter. Also referred to as biquad filter, this is a basic component for many digital filter implementations:

$$ y[n] = b_0 x[n] + b_1 x[n-1]+ b_2 x[n-2] - a_1 y[n-1] - a_2 y[n-2] $$

Or short:

$$ y[n] = \sum\limits_{i=0}^{i=N} b_i x[n-i] - \sum\limits_{i=1}^{i=N} a_i y[n-i] $$

Implementation Structure

The above difference equation can be transferred into different implementation structures. These structures can be directly implemented in actual code. The following one is the direct form 1:


A filter of the order $N$ can be expressed as a polynominal, using the Z-operator $z = e^{sT}$:

$$ h[n] = \frac{\sum\limits_{n=0}^{n=N} b_n z^{-n}}{\sum\limits_{n=1}^{n=N} a_n z^{-n}} $$

Pole-Zero Plot

IIR filters have roots in the demoninator, called zeros and in the nominator, called poles:

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