Filtering Fundamentals

Est. read time: 1 minute | Last updated: October 16, 2024 by John Gentile

Contents

FIR Filters
Filter Design
- Complex-Valued Filters
- References
Parallel/Vectorized Filter/Convolution
References

from rfproto import filter

FIR Filters

The discrete-time convolution of $M$ filter coefficients with input samples $x[n]$ can be seen as:

y[n] = x[n] * h[n] = \sum_{k=0}^{M-1}h[k]x[n-k]

Picture link

test_filt = filter.fir_filter([0, 0, 1, 0, 0])
for i in range(10):
    print(test_filt.step(i+1))

0.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0

Filter Design

Complex-Valued Filters

Rarely you’ll need to use a complex-valued filter- often you’re looking to apply a filter on complex-valued input signals. In this case, the same real filter tap values and convolution process can happen in parallel on both real and imaginary parts of the signal.

Complex Filter Design

References

SciPy Signal - Filter Design Methods

Parallel/Vectorized Filter/Convolution

References

FIR Compiler LogiCORE IP Product Guide • FIR Compiler (PG149) • Reader • AMD Adaptive Computing Documentation Portal