EE3TP4_20_CT_IdealFilters_Lecture 28

# EE3TP4_20_CT_IdealFilters_Lecture 28 - 5.3 Ideal Filters...

This preview shows pages 1–4. Sign up to view the full content.

5.3 Ideal Filters Often we have a scenario where we have a “good” signal, x g ( t ), corrupted by a “bad” signal, x b ( t ), and we want to use an LTI system to remove (or filter out) the bad signal, leaving only the good signal. How do we do this? What H ( ϖ ) do we want? h ( t ) H ( ) x t = x g t  x b t y t = x g t “Filter” Desired Output These overheads were originally developed by Mark Fowler at Binghamton University, State University of New York.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
X g ω ∣ − ω X b ω ∣ Case #1: is a low-frequency signal x g t is a high-frequency signal x b t X ω ∣ Spectrum of the Input Signal In this case, we want a filter like this: H ω ∣ H ω ∣= { 1, − ω  0, otherwise Mathematically: “Passband” “Stopband” ω − − ω
( ϖ ) X ω Y ω = X ω H ω Then: Y ω ∣=∣ H ω

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### Page1 / 10

EE3TP4_20_CT_IdealFilters_Lecture 28 - 5.3 Ideal Filters...

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online