wins short course 1 wins network signal processing network signal processing research review...
DESCRIPTION
WINS Short Course 3 Sensor.com Signal Characteristics Seismic Signal PSD –observatory class geophone Mark Products L-4 vertical axis 2 kg instrument –3000 sps sampling system Tracked vehicle signatures –Important features at less than 200 Hz frequency (Hz) ground velocity power spectral densityv tracked vehicle I tracked vehicle IITRANSCRIPT
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WINS Network Signal Processing
Network Signal Processing Research ReviewSenseIT PI MeetingOctober 7-8, 1999
Marina Del Rey
Presentedto
Dr. Sri KumarDARPA/ITO
bySensor.com
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Outline
• Network Signal Processing– Network and Database Methods for Threat
Detection– Network and Database Methods for Threat
Identification– Example application– Networked Signal Processing Choices for
SenseIT
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Signal Characteristics
• Seismic Signal PSD– observatory class
geophone• Mark Products L-4• vertical axis• 2 kg instrument
– 3000 sps sampling system
• Tracked vehicle signatures– Important features at
less than 200 Hz
frequency (Hz)
grou
nd v
eloc
ity
pow
er s
pect
ral d
ensi
tyv
tracked vehicle I
tracked vehicle II
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Threat Detection
• Heterogeneity:– signal types– signal generation– signal propagation– signal-to-noise ratio
• Multiple threats• Select optimal
– algorithms– sensors– distribution
frequency (Hz)
grou
nd v
eloc
ity
pow
er s
pect
ral
dens
ityv
time
Infr
ared
mot
ion
sens
or s
igna
l
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• Signal Evolution– Resolve unique features:
• Approach, Arrival, Departure• Speed• Environment
– Combine• Seismic• Magnetic• Infrared Motion
Threat Identification
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Threat Identification
• Current research area– template matching methods– time domain– frequency domain– wavelet
• Challenges (continued)– multiple signal characteristics:
• continuous waveform (vehicle signature)• impulse signature (infrared, magnetic, seismic
signals due to footfalls)
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Network and Database Methods
Threat Detection• Event Sequence: Query for
– threat passage– sensor cueing
• Event History: Query for– long term patterns– pattern deviations
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Network and Database Methods
Threat Identification• Node data: Query for
– sensors brought to bear on target– detection range/signal-to-noise ratio– signal evolution
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Retaskable/Reprogrammable
• Example: Exploit (leverage signal search engine methods)
decision
data flow
signal
decision
low powercorrelator
signal
correlatorlibrary
memory
provencorrelators
assessment
Signal Search EngineConventional
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CorrelatorCorrelator
Correlation (Inner Product):Correlation (Inner Product):
* * * *
Correlation ValueCorrelation Value
Shift “Correlator” over one sample, recorrelate, Shift “Correlator” over one sample, recorrelate, and repeat. Produces a “correlation signal”. and repeat. Produces a “correlation signal”. Classification may include RMS value of Classification may include RMS value of correlation signal and its time evolution.correlation signal and its time evolution.
Threat Identification
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• Example results - ARL ACIDS Database– acoustic data set acquired for:
• light and heavy wheeled vehicles• tracked vehicles
– 1000 sps sampling– system was “trained” on signal library– system “unknown” signals
Threat Identification
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Threat Identification• Terrain: Desert
• Signals: Departure• Class Errors: 0%
CorrelatorsSignals
Correlators
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Threat Identification
• Terrain: Artic• Signals: Approach• Class Errors: 5.6%
CorrelatorsSignals
Correlators
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Threat Identification
• Limitations of current systems– Incomplete signal processing library
• Energy cost associated with failure to identify:– need to migrate an entire data set to a remote
user• Goal:
– exploit network and databases to upgrade any node
– replace data set communication with communication of code, protocols, ...
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Threat Identification
• Network Signal Processing and Database Methods– Identify inadequately performing nodes– Service requests for upgraded library elements– Exploit new data and new measurements– Exploit remote resources and decisions– Update nodes with appropriate individual library
selections – Leverage database concurrency methods, atomic
transfer– Manage asynchronous processing
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SenseIT Standards
Network Signal Processing Databases
• Choices– Signal recordset format– Filter characteristics recordset format– Mobile signal processing code recordset
format