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a low-pass filter is a filter that allows signals below a cutoff frequency (known as the passband) and attenuates signals above the cutoff frequency (known as the stopband).

low-pass filters, especially  or , are often used to clean up signals, remove noise, create a smoothing effect, perform data averaging, and design decimators and interpolators. low-pass filters produce slow changes in output values to make it easier to see trends and boost the overall  with minimal signal degradation.

you can use matlab^® to design finite impulse response (fir)-based and infinite impulse response (iir)-based filters, two common low-pass filter methods.

fir filters are very attractive because they are inherently stable. they can be designed to have linear phase that introduces a delay in the filtered signal while maintaining the waveform shape. nonetheless, these filters can have long transient responses and might prove computationally expensive in certain applications. fir filters are useful in audio, biomedical, radar, and other applications where the waveform shape provides useful information. common design methods for low-pass fir-based filters include , , and .

iir filters are useful when computational resources are at a premium. however, stable, causal iir filters do not have perfectly linear phase. iir filters are commonly used in audio equalization, biomedical sensor signal processing, iot/iiot smart sensors, and high-speed telecommunication/rf applications. design methods for iir-based filters include , chebyshev ( and ), and .

the function in signal processing toolbox™ is particularly useful to quickly filter signals. you can use and other algorithm-specific (butter, fir1) functions when more control is required on parameters such as filter type, filter order, and attenuation. for more information on , see signal processing toolbox™.