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Outline
• Neyman-Pearson Theorem
• Detector Performance
• Irrelevant Data
• Minimum Probability of Error
• Bayes Risk
• Multiple Hypothesis Testing
Chapter Goal
• State basic statistical groundwork for detectors design
• Address simple hypothesis , where PDFs are known (Chapter 6 will cover the case of unknown PDFs)
• Introduction to the classical approach based on Neyman-Pearson and Bayes Risk– Bayesian methods employ prior knowledge of the hypotheses
– Neyman-Pearson has no prior knowledge
Bayes vs Newman-Pearson
• The particular method employed depends upon our willingness to incorporate prior knowledge about the probabilities of ocurrence of the various hypotheses
• Typical cases– Bayes : Communication systems, Pattern recognition
– NP : Sonar, Radar
Nomenclature
2 2( , ) : G a u s s ia n P D F w ith m e a n a n d v a r ia n c e
: H y p o th e s is i
( | ) : P ro b a b i li ty o f o b s e rv in g x u n d e r a s s u m p rio n H
( ; ) : P ro b a b i li ty o f d e te c t in g H , w h e n i t i s H
( | ) : P r
i
i i
i j i j
i j
H
p x H
P H H
P H H
o b a b i li ty o f d e te c t in g H , h e n H w a s o b s e rv e d
( ) : L ik e lih o o d o f x
i jw
L x
Nomenclature cont...
• Note that is the probability of deciding Hi, if Hj is true.
• While assumes that the outcome of a probabilistic experiment is observed to be Hj and that the probability of deciding Hi is conditioned to that outcome.
( | )i j
P H H
( ; )i j
P H H
Neyman-Pearson Theorem cont...
• It is no possible to reduce both error probabilities simultaneously
0
1
: [ 0 ] [0 ]
: [ 0 ] [ 0 ] [0 ]
H x w
H x s w
• In Neyman-Pearson the approach is to hold one error probability while minimizing the other.
Neyman-Pearson Theorem cont...
• We wish to maximize the probability of detection
• And constraint the probability of false alarm
• By choosing the threshold
1 1( ; )
DP P H H
1 0( ; )
F AP P H H
γ
Neyman-Pearson Theorem cont...
• To maximize the probability of detection for a given probability of false alarm decide for H1 if
• with
1
0
( ; )( )
( ; )
p x HL x
p x H
Likelihood ratio test
0
{ : ( ) }
( ; )
x L x
F AP p x H d x
Likelihood Ratio
• The LR test rejects the null hypothesis if the value of this statistic is too small.
• Intuition: large Pfa -> small gamma -> not much larger p(x,H1) than p(x;H0)
• Limiting case: Pfa->1
• Limiting case: Pfa->0
1
0
( ; )( )
( ; )
p x HL x
p x H
Neyman-Pearson Theorem cont...
• Example 1
• Maximize
• Calculate the threshold so that3
1 0F A
P
1 1( ; )
DP P H H
Question
Answer is: Only if we allow for a larger Pfa.
NP gives the optimal detector!
By allowing a Pfa of 0.5, the Pd is increased to 0.85
How can Pd be improved?
System Model
• In the General case
• Where the signal s[n]=A for A>0 and w[n] is WGN with variance σ².
• It can be shown that the sum of x is a sufficient statistic
• The summation of x[n] is sufficient.
Detector Performance
• In a more general definition
Or
where
Energy-to-noise-ratio
Deflection Coefficient
Detector Performance cont...
• An alternative way to present the detection performance
Receiver Operating Characteristic (ROC)
γ ' in c reas in g
γ '
Irrelevant data
• Some data not belonging to the desired signal might actually be useful.
• The observed data-set is
• If
• Then the reference noise can be used to improve the detection. The perfect detector would become
[ 0 ] [ 0 ]2
R
AT x w
{ [0 ], [1], ..., [ 1], [0 ], [1], ..., [ 1]R R R
x x x N w w w N
0
[ ] [ ]:
[ ]R
x n w nH
w w n
1
[ ] [ ]:
[ ]R
x n A w nH
w w n
Irrelevant data cont...
• Generalizing, using the NP theorem
• If they are un-correlated
• Then, x2 is irrelevant and
1 2 1( , ) ( )L x x L x
Minimum Probability Error
• The probability of error is defined with Bayesian paradigm as
• Minimizing the probability of error, we decide H1 if
• If both prior probabilities are equal we have
Minimum Probability Error example
• An On-off key communication system , where we transmit either
• The probability of transmitting 0 or A is the same ½. Then, in order to minimize the probability of error γ=1
0 1[ ] 0 o r [ ]S n S n A
Minimum Probability Error example ...
1A n a lo g o u s to N P , w e d ec id e if
2AH x
Then we calculate the Pe
Deflection Coefficient
The probability error decreases monoto
nically with respect of the deflection
coefficient
Bayes Risk
• Bayes Risk assigns costs to the type of errors, denotes the cost if we decide but is true. If no error is made, no cost is assigned.
• The detector that minimizes Bayes risk is the following
i jC
iH
jH
Multiple Hyphoteses testing
• If we have a communication system, M-ary PAM (pulse amplitud modulation) with M=3.
Multiple Hyphoteses testing cont...
• The conditional PDF describing the communication system is given by
• In order to maximize p(x|Hi), we can minimize
• There are six types of Pe, so Instead of calculating Pe, we calculate Pc=1-Pe, the probability of correct desicion
• And finally
Multiple Hyphoteses testing cont...
Multiple Hyphoteses testing cont...
Binary Case PAM Case
2
2
4
3 4e
N AP Q
2
24
e
N AP Q
In the digital communications world, there is a difference in the
energy per symbol, which is not considered in this example.