Summary: Infinite impulse response (IIR) filters are an alternative to finite impulse response (FIR) filters. Often, an IIR implementaion can meet a given filter specification with less computation than an FIR implementation, but IIR filters induce nonlinear phase and are more sensitive to numerical problems.
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Like finite impulse-response (FIR) filters, infinite impulse-response (IIR) filters are linear time-invariant (LTI) systems that can recreate a large range of different frequency responses. Compared to FIR filters, IIR filters have both advantages and disadvantages. On one hand, implementing an IIR filter with certain stopband-attenuation and transition-band requirements typically requires far fewer filter taps than an FIR filter meeting the same specifications. This leads to a significant reduction in the computational complexity required to achieve a given frequency response. However, the poles in the transfer function require feedback to implement an IIR system. In addition to inducing nonlinear phase in the filter (delaying different frequency input signals by different amounts), the feedback introduces complications in implementing IIR filters on a fixed-point processor. Some of these complications are explored in IIR Filtering: Filter-Coefficient Quanitization Exercise in MATLAB.
Later, in the processor exercise, you will explore the
advantages and disadvantages of IIR filters by implementing
and examining a fourth-order IIR system on a fixed-point DSP.
The IIR filter should be implemented as a cascade of two
second-order, Direct Form II sections. The data flow for a
second-order, Direct-Form II section, or bi-quad,
is shown in Figure 1. Note that in Direct Form
II, the states (delayed samples) are neither the input nor the
output samples, but are instead the intermediate values
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