2.2) Reducing the undesired effects of noise when a given signal is corrupted by noise with known (partially) statistics. Let x, be the original and x be the measured signal which is affected by additive white Gaussian noise. x[n] = x,[n] + N(n) where N(n) is the noise. Our task is to use moving average filter to extract xo[n] from the observed signal x[n]. Define the original signal as xo[n] = cos (n) + 2cos (n). Define the domain as – 1000
2.2) Reducing the undesired effects of noise when a given signal is corrupted by noise with known (partially) statistics. Let x, be the original and x be the measured signal which is affected by additive white Gaussian noise. x[n] = x,[n] + N(n) where N(n) is the noise. Our task is to use moving average filter to extract xo[n] from the observed signal x[n]. Define the original signal as xo[n] = cos (n) + 2cos (n). Define the domain as – 1000
Introductory Circuit Analysis (13th Edition)
13th Edition
ISBN:9780133923605
Author:Robert L. Boylestad
Publisher:Robert L. Boylestad
Chapter1: Introduction
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Data signal Processing EENG420 - Electrical Engineering
![2.2) Reducing the undesired effects of noise when a given signal is corrupted
by noise with known (partially) statistics.
Let x, be the original and x be the measured signal which is affected by
additive white Gaussian noise.
x[n] = x,[n] + N(n)
%3D
where N(n) is the noise. Our task is to use moving average filter to extract
xo[n] from the observed signal x[n].
Define the original signal as xo[n] = cos (n) + 2cos (
Define the domain as – 1000 <n < 1000
i)
ii)
iii)
Generate the noise signal by using randn command
iv)
Add noise signal to the original signal and obtain observed signal
x[n].
v)
Apply the moving average filter to x[n] and obtain the output
y[n].
vi)
Implement filter for different M, and M2, i.e M, = M2 =
4,10,20 ... and observe the output.
vii)
Plot xo[n], x[n] and y[n] for every step. Comment on your
results.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fb4f115b3-e841-4a1c-be62-348e12154942%2F54b83199-335e-4d6f-8afd-f28ebc2c5e48%2Fjpommdq_processed.jpeg&w=3840&q=75)
Transcribed Image Text:2.2) Reducing the undesired effects of noise when a given signal is corrupted
by noise with known (partially) statistics.
Let x, be the original and x be the measured signal which is affected by
additive white Gaussian noise.
x[n] = x,[n] + N(n)
%3D
where N(n) is the noise. Our task is to use moving average filter to extract
xo[n] from the observed signal x[n].
Define the original signal as xo[n] = cos (n) + 2cos (
Define the domain as – 1000 <n < 1000
i)
ii)
iii)
Generate the noise signal by using randn command
iv)
Add noise signal to the original signal and obtain observed signal
x[n].
v)
Apply the moving average filter to x[n] and obtain the output
y[n].
vi)
Implement filter for different M, and M2, i.e M, = M2 =
4,10,20 ... and observe the output.
vii)
Plot xo[n], x[n] and y[n] for every step. Comment on your
results.
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