Stochastic processes

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Stochastic processes
Lecture 7
Linear time invariant systems
1
Random process
2
1st order Distribution & density
function
First-order distribution
First-order density function
3
2end order Distribution & density
function
2end order distribution
2end order density function
4
EXPECTATIONS
• Expected value
• The autocorrelation
5
Some random processes
•
•
•
•
•
•
•
•
Single pulse
Multiple pulses
Periodic Random Processes
The Gaussian Process
The Poisson Process
Bernoulli and Binomial Processes
The Random Walk Wiener Processes
The Markov Process
6
Sxx(f)
Recap: Power spectrum density
f
7
Power spectrum density
• Since the integral of the squared absolute Fourier
transform contains the full power of the signal it is a
density function.
•
So the power spectral density of a random process is:
  =


→∞


−
  −2 
2
2
• Due to absolute factor the PSD is always real
8
Power spectrum density
• The PSD is a density function.
– In the case of the random process the PSD is the
density function of the random process and not
necessarily the frequency spectrum of a single
realization.
• Example
– A random process is defined as
X  = sin( )
– Where ωr is a unifom distributed random variable
wiht a range from 0-π
– What is the PSD for the process and
– The power sepctrum for a single realization
9
Properties of the PSD
1. Sxx(f) is real and nonnegative
2. The average power in X(t) is given by:
  2 () =  0 =
∞
  
−∞
3. If X(t) is real Rxx(τ) and Sxx(f) are also even
 − =  
4. If X(t) has periodic components Sxx(f)has impulses
5. Independent on phase
10
Wiener-Khinchin 1
• If the X(t) is stationary in the wide-sense the
PSD is the Fourier transform of the
Autocorrelation
11
Wiener-Khinchin
Two method for estimation of the PSD
Fourier Transform
X(f)
|X(f)|2
X(t)
Sxx(f)
X(t)
f
Fourier Transform
t
Rxx()
Sxx(f)
Autocorrelation
f

12
The inverse Fourier Transform of the
PSD
• Since the PSD is the Fourier transformed
autocorrelation
• The inverse Fourier transform of the PSD is the
autocorrelation
13
Cross spectral densities
• If X(t) and Y(t) are two jointly wide-sense
stationary processes, is the Cross spectral
densities
• Or
14
Properties of Cross spectral densities
1. Since
is
2. Syx(f) is not necessary real
3. If X(t) and Y(t) are orthogonal
Sxy(f)=0
4. If X(t) and Y(t) are independent
Sxy(f)=E[X(t)] E[Y(t)] δ(f)
15
Cross spectral densities example
• 1 Hz Sinus curves in white noise
  = sin 2  + 3 ()
  = sin 2  +  2 + 3 ()
Welch Cross Power Spectral Density Estimate
Where w(t) is Gaussian noise
5
0
X(t)
10
0
-10
0
5
10
t (s)
Signal Y(t)
15
20
Y(t)
10
-5
-10
-15
-20
-25
0
-10
Power/frequency (dB/Hz)
Signal X(t)
-30
0
5
10
t (s)
15
20
0
5
10
15
Frequency (Hz)
20
25
16
The periodogram
The estimate of the PSD
• The PSD can be estimate from the
autocorrelation
−1
 [] −ω
 ω =
=−+1
• Or directly from the signal
 ω =
1

2
−1
 [] −ω
=0
17
Bias in the estimates of the
autocorrelation
N=12
−  −1
  =
  [ + ]
=0
Autocorrelation
Autocorrelation
M=-10
M=0
M=4
222
888
111
666
000
444
-1-1
-1
-2-2
-2
-10
-10
-10
-5
-5
-5
000
555
nnn
10
10
10
15
15
15
20
20
20
222
000
222
111
-2
-2
-2
000
-4
-4
-4
-1-1
-1
-2-2
-2
-10
-10
-10
-5
-5
-5
000
555
n+m
n+m
n+m
10
10
10
15
15
15
20
20
20
-6
-6
-6
-15
-15
-15
-10
-10
-10
-5
-5
0
5
10
15
18
Variance in the PSD
• The variance of the periodogram is estimated
to the power of two of PSD
  
=  ()
2
Realization 1
10
0
5
-5
True PSD
1
0
0.8
Sxx(f)
PSD: Realization 1
5
5
t (s)
Realization 2
10
0
5
10
0
5
0
50
100
150
f (Hz)
PSD: Realization 2
200
50
200
0.6
0.4
-5
0.2
0
0
50
100
f (Hz)
150
200
0
5
t (s)
Realization 3
10
0
5
10
0
5
-5
0
5
t (s)
10
0
0
0
100
150
f (Hz)
PSD: Realization 3
50
100
f (Hz)
150
200
19
Averaging
• Divide the signal into K segments of M length
 =   − 1  + 1:  
1≤≤
• Calculate the periodogram of each segment

1
ω =

2
−1
  [] −ω
=0
• Calculate the average periodogram
1
 [ω] =


 [ω]
=0
20
Illustrations of Averaging
X(t)
2
0
-2
-4
0
1
2
10
3
4
5
4
6
7
10
8
9
0
100
10
6
4
5
2
5
2
0
0
100
200
0
0
100
200
0
0
100
200
0
200
10
5
0
0
50
100
f (Hz)
150
200
21
PSD units
• Typical units:
2/Hz or dB V/Hz
• Electrical
measurements:
V
.
• Sound: Pa2/Hz or dB/Hz
• How to calculate dB I a power spectrum:
PSDdB(f) = 10 log10 { PSD(f) }
22
Agenda (Lec. 7)
• Recap: Linear time invariant systems
• Stochastic signals and LTI systems
–
–
–
–
Mean Value function
Mean square value
Cross correlation function between input and output
Autocorrelation function and spectrum output
• Filter examples
• Intro to system identification
23
Focus continuous signals and system
Continuous signal:
x(t)
1
0
-1
0
20
40
60
80
100
8
10
t (s)
Discrete signal:
1
x[n]
0.5
0
-0.5
-1
0
2
4
6
n
24
Systems
25
Recap: Linear time invariant systems
(LTI)
• What is a Linear system:
– The system applies to superposition
T a x1 (t )  b x2 (t )  a T x1 (t ) b T x2 (t )
Nonlinear systems
Linear system
20
25
18
20
16
15
14
10
y(t)
y(t)
12
10
5
8
0
6
-5
4
-10
x[n] 2
-15
x[n]
2
0
20 log(x[n])
0
1
2
3
x(t)
4
5
-20
0
1
2
3
x(t)
4
5
26
Recap: Linear time invariant systems
(LTI)
• Time invariant:
• A time invariant systems is independent on explicit time
–
(The coefficient are independent on time)
• That means
If:
Then:
y2(t)=f[x1(t)]
y2(t+t0)=f[x1(t+t0)]
The same to Day tomorrow and in 1000 years
A non Time invariant
20 years
45 years
70 years
27
Examples
• A linear system
y(t)=3 x(t)
• A nonlinear system
y(t)=3 x(t)2
• A time invariant system
y(t)=3 x(t)
• A time variant system
y(t)=3t x(t)
28
The impulse response
The output of a system if Dirac delta is input
 (t )
h[n]  f [ (t )]
h(t )
Impuls response
Impuls
x(t)
T{∙}
y(t)
inf
0
0
-10
-5
0
5
t
10
15
20
-10
-5
0
5
t
10
15
20
Convolution
• The output of LTI system can be determined
by the convoluting the input with the impulse
response
30
Fourier transform of the impulse
response
• The Transfer function (System function) is the
Fourier transformed impulse response
• The impulse response can be determined from
the Transfer function with the invers Fourier
transform
31
Fourier transform of LTI systems
• Convolution corresponds to multiplication in
the frequency domain
Time domain
Output
Impuls response
Input
3
2
1
1
0
*
-1
-2
-3
-10
-5
0
5
t
10
15
=
0
y(t)
2
y(t)
x(t)
3
-2
-3
-10
20
-10
0
-1
-5
0
5
t
10
15
20
-5
0
5
t
10
15
20
Frequency domain
1.5
3000
1200
2500
1000
1
1000
=
600
400
0.5
200
500
0
-2
|Y(f)|
x
1500
800
|H(f)|
|X(f)|
2000
-1
0
f (Hz)
1
2
0
-2
-1
0
f (Hz)
1
2
0
-2
-1
0
f (Hz)
1
2
32
Causal systems
• Independent on the future signal
y(t)
Impuls response
0
-10
-5
0
5
t
10
15
20
33
Stochastic signals and LTI systems
• Estimation of the output from a LTI system
when the input is a stochastic process
Α is a delay factor like τ
34
Statistical estimates of output
• The specific distribution function fX(x,t) is
difficult to estimate. Therefor we stick to
– Mean
– Autocorrelation
– PSD
– Mean square value.
35
Expected Value of Y(t) (1/2)
• How do we estimate the mean of the output?
∞
∞
 
=
  −  ℎ  
−∞
() =
  −  ℎ  
−∞
∞
 
=
   −  ℎ  
−∞
If mean of x(t) is defined as mx(t)
∞
 
=
 ( − )ℎ  
−∞
36
Expected Value of Y(t) (2/2)
If x(t) is wide sense stationary
  −  =   = 
(   )
∞
 =   
=
∞
 ( − )ℎ  
−∞
 =   
∞
= 
ℎ  
−∞
( − )ℎ  
−∞
Alternative estimate:
At 0 Hz the transfer function is equal to the DC gain
∞
ℎ   = (0)
−∞
Therefor:
 =   
=  (0)
37
Expected Mean square value (1/2)
∞
 
2
() =
=    
−∞
∞
 
2
=
∞
  − 1 ℎ 1 1
−∞
∞
 
2
  −  ℎ  
  − 2 ℎ 2 2
−∞
∞
=
  − 1   − 2 ℎ 1 ℎ 2 12
−∞ −∞
∞
 
2
∞
=
   − 1   − 2 ℎ 1 ℎ 2 12
−∞ −∞
∞
 
2
∞
=
 ( − 1,  − 2) ℎ 1 ℎ 2 12
−∞ −∞
∞
 
2
=
∞
(1, 2) ℎ  − 1 ℎ  − 2 12
−∞ −∞
38
Expected Mean square value (2/2)
∞
 
2
∞
=
(1, 2) ℎ  − 1 ℎ  − 2 12
−∞ −∞
By substitution:
 =  − 1
 =  − 2
∞
 
2
∞
=
( − ,  − )ℎ  ℎ  12
−∞ −∞
If X(t)is WSS
∞
 
2
=
∞
 ( − ) ℎ  ℎ  12
−∞ −∞
Thereby the Expected Mean square value is independent on time
39
Cross correlation function between
input and output
• Can we estimate the Cross correlation between input
and out if X(t) is wide sense stationary
∗
Input
  + ,  =    +   ()
3
2
x(t)
1
∞
0
  + ,  = 
-1
-2
-3
-10
-5
0
5
t
10
15
−∞
20
∞
Output
3
  + ,  = 
2
y(t)
1
  −  +  ℎ    ∗ ()
0
  −  +   ∗ ()ℎ  
−∞
-1
-2
-3
-10
-5
0
5
t
10
15
  =    +   ()
20
∞
Cross-correlation between y(t) and x(t)
1500
  =
1000
Rxy()
500
  −  ℎ   =   ∗ ℎ()
−∞
0
-500
-1000
-1500
-30
-20
-10
0
 (s)
10
20
30
Thereby the cross-correlation is the convolution between
40
the auto-correlation of x(t) and the impulse response
Autocorrelation of the output (1/2)
  =   + ,  =    +  ()
∞
Y(t) and Y(t+τ) is :
( + ) =
  +  −  ℎ  
−∞
∞
() =
  −  ℎ  
−∞
∞

−∞
  = 
  = 
∞ ∞

−∞ −∞
∞
  =
 +  −  ℎ  
∞

−∞
 −  ℎ  
 +  −    −  ℎ  ℎ  
∞
[  +  −    −  ]ℎ  ℎ  
−∞ −∞
41
Autocorrelation of the output (2/2)
∞
  =
∞
[  +  −    −  ]ℎ  ℎ  
−∞ −∞
By substitution: α=-β
∞
  =
∞
[  +  −    +  ]ℎ  ℎ − 
−∞ −∞
  =   ∗ ℎ(−)
Remember:   =   ∗ ℎ  =
∞
  −  ℎ  
−∞ 
Autocorrelation of y(t)
1000
  =   ∗ ℎ() ∗ ℎ(−)
Rxy()
500
0
-500
-1000
-30
-20
-10
0
 (s)
10
20
30
42
Spectrum of output
• Given:
  =   ∗ ℎ  ∗ ℎ(−)
|  |2 =    ∗ ()
• The power spectrum is
  =      ∗ ()
  =   |  |2
5
6
10
x 10
12
x 10
1.5
10
x
4
=
6
4
0.5
2
0
-2
Syy(f)
8
1
6
|H(f)|2
Sxx(f)
8
2
-1
0
f (Hz)
1
2
0
-2
-1
0
f (Hz)
1
2
0
-2
-1
0
f (Hz)
1
43
2
Filter examples
44
Typical LIT filters
• FIR filters (Finite impulse response)
• IIR filters (Infinite impulse response)
– Butterworth
– Chebyshev
– Elliptic
45
Ideal filters
• Highpass filter
• Band stop filter
• Bandpassfilter
Filter types and rippels
47
Analog lowpass Butterworth filter
• Is ”all pole” filter
– Squared frequency transfer function
H( f ) 
2
1
2N
1   f / fc 
• N:filter order
• fc: 3dB cut off frequency
• Estimate PSD from filter
1
S yy ( f )  S xx ( f )
2N
1   f / fc 
Chebyshev filter type I
• Transfer function
Hf  
2
1
1   2TN2  f / f p 
• Where ε is relateret
to ripples in the pass
band
• Where TN is a N order
polynomium
 cos(N cos1 x) x  1
TN ( x)  
1
cosh(N cosh x) x  1
Transformation of a low pass filter to
other types (the s-domain)
Filter type
Lowpas>Lowpas
Transformation
p
s
s
' p
Lowpas>Highpas
 p ' p
Lowpas>Highpas
Lowpas>Stopband
p
Old Cutoff frequency
' p New Cutoff frequency
s
s
s 2  l u
s  p
s ( u   l )
s ( u   l )
s  p 2
s  l u
New Cutoff frequency
' p
' p
l ,  u
l ,  u
l :
Lowest Cutoff frequency
u :
Highest Cutoff frequency
Discrete time implantation of filters
• A discrete filter its Transfer function in the zdomain or Fourier domain
b0  b1 z 1  b2 z 2 ´.......bm z  M
Y ( z)
 H ( z) 
X ( z)
a0  a1 z 1  a2 z  2 ´.......am z  M
– Where bk and ak is the filter coefficients
• In the time domain:
y[n]  b0 x[n]  b1 x[n  1]  b2 x[n  2]  ......  aM x[n  M ]
 a1 y[n  1]  a2 y[n  2]  ......  aM y[n  M ]
51
Filtering of a Gaussian process
• Gaussian process
– X(t1),X(t2),X(t3),….X(tn) are jointly Gaussian for all t
and n values
• Filtering of a Gaussian process
y[n]  b0 w[n]  b1w[n  1]  b2 w[n  2]  ......  aM w[n  M ]
 a1 y[n  1]  a2 y[n  2]  ......  aM y[n  M ]
– Where w[n] are independent zero mean Gaussian
random variables.
52
The Gaussian Process
• X(t1),X(t2),X(t3),….X(tn) are jointly Gaussian for
all t and n values
• Example: randn() in Matlab
Histogram of Gaussian process
Gaussian process
700
5
4
600
3
500
2
1
400
0
300
-1
200
-2
100
-3
-4
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
0
-4
-3
-2
-1
0
1
2
3
4
5
The Gaussian Process and a linear time
invariant systems
• Out put = convolution between input and
impulse response
Gaussian input Gaussian output
Example
• x(t):
Gaussian process
5
Histogram of Gaussian process
4
700
3
600
2
500
1
400
0
-1
300
-2
200
-3
100
-4
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
0
-4
• h(t): Low pass filter
• y(t):
-2
-1
0
1
2
3
4
5
Histogram of y(t)
600
1.5
500
1
400
0.5
300
0
200
-0.5
100
0
-1.5
-1
-1.5
-3
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
-1
-0.5
0
0.5
1
1.5
Filtering of a Gaussian process
example 2
White noise
4
x(t)
2
0
-2
0
100
200
Magnitude (dB)
-4
Phase (degrees)
Band pass filter
300
400
500
t (ms)
600
700
800
900
1000
Transfere function of filter
0
-50
-100
0
100
200
300
Frequency (Hz)
400
500
0
100
200
300
Frequency (Hz)
400
500
0
-500
-1000
Output
1
y(t)
0.5
0
-0.5
-1
0
100
200
300
400
500
t (ms)
600
700
800
900
1000
56
Intro to system identification
• Modeling of signals using linear Gaussian
models:
• Example: AR models
y[n]  a1 y[n  1]  a2 y[n  2]  ......  aM y[n  M ]  w[n]
• The output is modeled by a linear
combination of previous samples plus
Gaussian noise.
57
Modeling example
• Estimated 3th order model
y[n]  -2.6397 y[n  1]  2.3903 y[n  2] - 0.7299 y[n  3]  w[n]
Output
1
Output
0.5
-0.5
signal
points used for prediction
Prediction
True point
y(t)
-1
0.35
0
100
200
300
400
500
t (ms)
600
700
800
900
1000
0.3
Output
0.25
451
0.294
451.5
452
452.5
t (ms)
453
453.5
454
0.292
0.29
y(t)
y(t)
0.4
0
0.288
w[n]
0.286
0.284
0.282
58
453.98 453.99
454
t (ms)
454.01 454.02
Agenda (Lec. 7)
• Recap: Linear time invariant systems
• Stochastic signals and LTI systems
–
–
–
–
Mean Value function
Mean square value
Cross correlation function between input and output
Autocorrelation function and spectrum output
• Filter examples
• Intro to system identification
59
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