nonlinear and time variant signal processing n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Nonlinear and Time Variant Signal Processing PowerPoint Presentation
Download Presentation
Nonlinear and Time Variant Signal Processing

Loading in 2 Seconds...

play fullscreen
1 / 12

Nonlinear and Time Variant Signal Processing - PowerPoint PPT Presentation


  • 90 Views
  • Uploaded on

Nonlinear and Time Variant Signal Processing. R.C. Maher ECEN4002/5002 DSP Laboratory Spring 2003. Introduction. Most of the signal processing algorithms considered in this course are linear and time invariant (LTI).

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Nonlinear and Time Variant Signal Processing' - breena


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
nonlinear and time variant signal processing

Nonlinear and Time VariantSignal Processing

R.C. Maher

ECEN4002/5002 DSP Laboratory

Spring 2003

introduction
Introduction
  • Most of the signal processing algorithms considered in this course are linear and time invariant (LTI).
  • One nonlinear example: the “noise gate” considered in Lab #6: output depends on signal amplitude
  • Other important nonlinear systems: modulation (AM, PM, FM), automatic gain control, pulse shaping, and adaptive filtering

Nonlinear Signal Processing R. C. Maher

automatic gain control
Automatic Gain Control
  • Gain control circuits include
    • Compressor: decrease dynamic range by reducing gain for high amplitude signals
      • Limiter: extreme form of compressor
    • Expander: increase dynamic range by reducing gain for low amplitude signals
      • Gate: extreme form of expander

Nonlinear Signal Processing R. C. Maher

gain control cont
Gain Control (cont.)
  • Gain control framework
  • c[n] can be |x[n]|, envelope of x[n], RMS value of x[n], etc.
  • Level detector typically has attack and release time constants

x[n]

y[n]=G[n] • x[n]

Level Detector

Gain

Controller

c[n]

G[n]

Nonlinear Signal Processing R. C. Maher

gain control cont1
Gain Control (cont.)
  • Simple envelope detectors:
  • Can also use |x[n]|2

if( |x[n]| > c[n-1] ) c[n]=  c[n]

else c[n]=  c[n]

(where a>1 and b<1)

Nonlinear Signal Processing R. C. Maher

gain control cont2
Gain Control (cont.)
  • Gain controller function
    • Compressor (r<1)

e.g., r=0.25

    • Expander (r>1)

e.g., r=4

Nonlinear Signal Processing R. C. Maher

gain curves
Gain Curves

Compressor

Expander

Output, dB

Output, dB

r=1

r<1

r<<1 (limiter)

r>1

r=1

r>>1 (gate)

threshold

Input, dB

threshold

Input, dB

Nonlinear Signal Processing R. C. Maher

communications am and fm
Communications: AM and FM
  • Generate AM and FM communication signals using synthesis techniques discussed before
  • Also, perform demodulation using a product detector (quadrature)

Lowpass Filter

Oscillator

Nonlinear Signal Processing R. C. Maher

waveshaping
Waveshaping
  • Apply a nonlinear “lookup” function

Output

Input

Nonlinear Signal Processing R. C. Maher

adaptive filters
Adaptive Filters
  • Basic adaptive filter is a linear system with time-varying coefficients
  • Coefficients (filter ‘weights’) are adjusted repeatedly at regular intervals according to an adaptive algorithm
  • Adaptive algorithm is generally designed to minimize the discrepancy (error) between the filter output and a reference signal

Nonlinear Signal Processing R. C. Maher

basic adaptive filter structure
Basic Adaptive Filter Structure

“Desired” or “reference” signal

d[n]

Filter response signal

Input signal

+

Adaptive Process

(digital filter with varying coefficients)

-

x[n]

y[n]

e[n]

Error signal

Nonlinear Signal Processing R. C. Maher

adaptive interference canceling
Adaptive Interference Canceling

Signal + Noise

d[n]=s[n]+e[n]

(s[n], e[n] uncorrelated)

Adaptive process tries to minimize E{e2[n]}

Filter response signal

Correlated Noise

+

Adaptive Process

(digital filter with varying coefficients)

-

ec[n]

y[n] e[n]

“Error” signal

e[n]  s[n]

Nonlinear Signal Processing R. C. Maher