Marco Duarte and Yu-Hen Hu Electrical and Computer …duarte/images/DBDF_IPSN_2003.pdfWireless...
Transcript of Marco Duarte and Yu-Hen Hu Electrical and Computer …duarte/images/DBDF_IPSN_2003.pdfWireless...
Distance Based DecisionFusion in a Distributed
Wireless Sensor Network
Marco Duarte and Yu-Hen HuElectrical and Computer Engineering
Introduction Increasingly feasible miniature ad-hoc sensor
network integration. Conventional centralized information and
data fusion are unsuited because of amountof data.
Distance-based fusion algorithm will selectsensors that give reliable results toparticipate.
Wireless Sensor Nodesand Network Sensor node: computer, battery, sensors,
transceivers. Deployment of sensor nodes outdoors. Regions: geographical clusters, each with a
manager node. Objective: detect vehicles as they pass
region, identify vehicle type, estimate location(EBL).
Sensor Network SignalProcessing Tasks
CFAR TargetDetection
Target Classification Target Localization Target Tracking
-200.0
-100.0
0.0
100.0
200.0
300.0
400.0
-200.0 -150.0 -100.0 -50.0 0.0 50.0 100.0
road
Node1-6
Node15
Node41-42
Node46-56
Node58-61
Sensor Network SignalProcessing Tasks Maximum Likelihood
50-dim spectral feature: Sampling frequency 4960 Hz 512-point FFT, resolution ~9
Hz Average first 100 points by
pairs, describes ~900 Hz Signal + noise classification
rate depends on SNR. SNR proportional to vehicle-
node distance.
11( | ) ~ exp ( ) ( )
2
T
k k kP x k x x x x
!" #! ! $ !% &
' (
!
Distance Based DecisionFusion
Current system architecture allows localizationprior to classification.
Accurate localization allows for estimation ofsensor-vehicle distance, estimation of probabilityof correct classification based on distance.
Data fusion: function of marginal results fromeach node.
Some events may be rejected by fusionalgorithm.
Measurements: classification and acceptancerates.
Decision Fusion x(i) is the feature vector for ith sensor, Ck is the kth
vehicle class, we must establish a function
g(zk) is the maximum function:
This is called Decision Fusion
( | (1), , ( )) ( | )
( ( ( | ( ))),1 )
k k
k
P x C x x N P x C x
f g P x C x i i N
! !
" ! # #
… !
1 ,( )
0 otherwise
k j
k
z z k jg z
> !"= #$
Distance Based DecisionFusion Multiplicative form: for statistically
independent feature vectors x(i), x(j).
Not realistic for sensor network. Additive form: weighted sum of marginal
posterior probabilities.
If wi=1 for all i, simple voting.
1
ˆ( | ) ( | ( )) i
N
w
k k
i
P x C x P x C x i
=
! = !"
1
ˆ( | ) ( ( | ( )))N
k i i k
i
P x C x w g P x C x i=
! = !"
Maximum A PosteriorDecision Fusion Weighting factor as function of distance and
SNR, determined using CFAR and EBLinformation.
We formulate a Maximum A Posterior (MAP)Probability Gating Network, using Bayesianestimation:
Probabilities from experiment data.ˆ( ) ( | , , ) ( | , ) ( , )
k k i i i i i iP x C P x C x d s P x d s P d s! = ! " "
Maximum A PosteriorDecision Fusion This amounts to assigning
weights:
Other methods: Distance gating:
Nearest Neighbor:
Baseline: simple voting(wi=1 for all i)
( | , ) ( , )i i i i iw P x d s P d s= !
1
0 otherwise
i max
i
d dw
!"= #$1 ,
0 otherwise
i j
i
d d j iw
! " #$= %&
Results
Closest node gives highest acceptance,classification rates for accurate localizationestimates
MAP Fusion has smaller dependence onlocalization error than other methods
Both of these methods can reducecommunication needed for decision