Post on 18-Jan-2016
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 1
Santa Fe, NM
January 16, 2002
Steve BeckJoe ReynoldsBrian Corser
Jorgen Harmse
Analysis and Applied Research Division6500 Tracor Lane, MS.1-8
Austin TX 78725
DARPA SenseIT ProgramCollaborative Signal andInformation Processing
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 2
Presentation Overview
• Accomplishments
• Collaborative System Architecture
• Sensors and Signal Analysis
• Collaborative Processing
• On-Going Work
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 3
Accomplishments: March 2001• Successful demonstration at MCAGCC in 29 Palms, CA
– Target tracking over a wireless sensor network.– Position estimates used to trigger imager.
• Implementation on Sensoria WINS 1.0
• BAE robust multi-modal detection and Kalman tracker.
• Penn St. interface code and ISI Directed Diffusion
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 4
Major Issues from March Demo
• Node Time and Node2Node Synchronization
• Filtered and Accurate GPS Positions
• Logging Errors, “Event” Messages, Heartbeat
• Software Setup and Control Mechanisms
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 5
Accomplishments: April-Nov. 2001
• Translated code from WINS1.0 to WINS2.0 (WinCE on MIPSR4000 to Linux on SH4) .
• Low level processing, improved repositories, APIs .
• Robust adaptive detection on three sensing modalities.
• Kalman tracker.
• Sensor tests and calibration experiments.
• Radio and multi-node timing tests.
• Integration and test procedures for CSIP.
• Participated in both Operational and Developmental SITEX02experiments at MCAGCC in 29 Palms, CA.
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 6
CollaborativeSystem
Architecture
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 7
Node Software Architecture• Robust adaptive multi-modal sensor data processing.• Five repositories to support collaboration, dynamic situation awareness, and decentralized data fusion.
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 8
Float and Gain Norm
Build Array HanningFFT
A-DET]
FFTRep
(float)
TSRep
(float)
Subscribers Subscribers
PreprocessorChannel 1
Data Filein Memory
Switch
High PassFilter (IIR)
Low Level Acoustic Processing
BAE SYSTEMSAPI Services
Publish and Subscribe Mechanism to the Data and Information Repositories Consistent with Directed Diffusion. Plug and Play
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 9
Sensors and Signal AnalysisSensors and
Signal Analysis
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 10
Signal Analysis: PIR Sensor
0 20 40 60 80 100 120-3000
-2000
-1000
0
1000
2000
3000Time Series PIR, File=W pirch03.bin
Time in seconds
Am
plit
ud
e
P IR #1PIR #2
Time Series Record Data File = matlab/wbk/pir_exp/wpirch03.binDisplay Program = matlab/wbk/pir_exp/viewpir2.m
L2R2.4 ft/sec
R2L2.8 ft/sec
L2R = Right to LeftR2L = Right to Left
L2R4.5 ft/sec
L2R5.6 ft/sec
L2R2.1 ft/sec
R2L4.9 ft/sec
R2L5.7 ft/sec
R2L2.8 ft/sec
P1
P2
10 ft3 ft
Experimental Set-up
L2R
7 ft
R2L
PIR
Passive InfraredMotion Detector
Difference BetweenThe Two Beams
Beam 1
Beam 2
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 11
InputDatax[10]
TargetModelFeatureExtraction
BackgroundModel Features
LogLikelihoodRatioTest
DetectionThresholds
ScoreNormalization
NormalizationParameters
Confidence
Binary Detection
Time Stamp
DetectionLatencyHeuristics
AmplitudeZero CrossingPolarity Direction
PIR Detection Processing
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 12
Acoustic and Seismic
InputDatax[1024]
BandpassFilter
TargetModelFeatureExtraction
LogLikelihoodRatioTest
ScoreNormalization
NormalizationParameters
Confidence
Binary Detection
Time Stamp
DetectionLatencyHeuristics
BackgroundModel Features
DetectionThresholds
Time Stamp
Speed and DistanceEstimation
Speed
Distance
Developmental
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 13
0.05 0.1 0.2 0.5 1 2 5 10 20 50 100 200 500 10000
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45Detection Histogram for File: 08020830-adet.lst
Pro
ba
bilit
y D
en
sit
y
Detection Output Value, EER Threshold=26.183
Signal Background EER Threshold
0.05 0.1 0.2 0.5 1 2 5 10 20 50 100 200 500 10000
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4Detection Histogram for File: 08020830-pdet.lst
Pro
ba
bilit
y D
en
sit
y
Detection Output Value, EER Threshold=54.7395
Signal Background EER Threshold
Seismic
0.05 0.1 0.2 0.5 1 2 5 10 20 50 100 200 500 10000
0.1
0.2
0.3
0.4
0.5
0.6
0.7Detection Histogram for File: 08020830-sdet.lst
Pro
ba
bilit
y D
en
sit
y
Detection Output Value, EER Threshold=12.749
Signal Background EER Threshold
Acoustic DET Curve
PIR
Detection for Multi-Modal Sensors
BackgroundTargetEER Point
Legend
0.002 0.005 0.01 0.02 0.05 0.1 0.2 0.5 1 2 5
0.002
0.005
0.01
0.02
0.05
0.1
0.2
0.5
1
2
5
False Alarm probability (in %)
Mis
s p
rob
ab
ility
(in
%)
DET Curve for File: 08020830-adet.lst
False Alarm Probability in %
Mis
s P
rob
abil
ity
in %
For SITEX00 data from August 2, 2000Acoustic EER = .008 per 0.5 seconds.Seismic and PIR EER < 10-5.Combined sensor detection EER < 10-5.
RobustLikelihoodRatioTestDetection
Acoustic
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 14
29 Palms Data Collection• Acoustic and Acoustic Arrays• Seismic and 3-Axis Seismic• PIR• Accelerometer with multiple ground couplings• Micro Radar• Magnetometer
Complementary Sensing Capabilities
Tetrahedron
Arrays
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 15
- 1 - 0 . 5 0 0 . 5 1 1 . 5 20
0 . 0 5
0 . 1
0 . 1 5
0 . 2
0 . 2 5
0 . 3
0 . 3 5
0 . 4
0 . 4 5P D F f o r T R U E a n d F A L S E S c o r e s , T e s t = L 0 3 T S T 0 1
Pro
ba
bili
ty
A u t o m a t ic S p e a k e r R e c o g n i t io n : L L R T S c o r e
P D F - F a ls e S c o r e sP D F - T r u e S c o r e s S c o r e - C h a n n e l M S c o r e - C h a n n e l T
Bayesian Conditional Normalization
Null Hypothesis(Non-Target)
Alt. Hypothesis(Target)
The black line = score with the SAME operating conditions as the training set.The green line = score from MISMATCHED operating conditions as the training set.
MatchedConditions
MismatchedConditions
• Adaptive to target and environmental priors• Outputs confidence levels conditioned on expectations• Empirically sound results - used in forensics.
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 16
Collaborative Signal and
Information Processing
Collaborative Signal and
Information Processing
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 17
Matching Pursuits - SITEX01/BAE2002PIR/Magnetometer discriminant functionComputational, memory requirements = mediumEfective for dynamic or multi-modal signalsClassifier design - Families of Basis Functions
Target Signal Classification
Rational Agent Classifier - BAE2001-2Dynamic Bayesian belief networksComputational, memory requirements = lowEffective for dynamic or multi-modal signalsClassifier design - qualitative description sufficient
Simple Entropy Classifier - SITEX00Renya -entropy discriminant functionComputational, memory requirements = lowNot effective for dynamic or multi-modal signalsClassifier design - data intensive
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 18
Hand-Off Tracking Node
*
**
**
**
*
*** *
*
*Road
Current Tracking Node
Zt,s,r
*
Contains Predicted Arrival Time (PAT), Expected Location (EL),
and network Track ID
tX
• Kalman Tracker• Probabilistic Multiple Hypothesis Tracker• Decentralized Information Tracker
Decentralized Trackers
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 19
Tracking System
Sensoria Wins2.0BAE Tracker
Cornell DatabaseISI Diffusion
Sensoria Wins2.0BAE Tracker
Cornell DatabaseISI Diffusion
Sensoria Wins2.0BAE Tracker
Cornell DatabaseISI Diffusion
Sensoria Wins2.0BAE Tracker
Cornell DatabaseISI Diffusion
Sensoria Wins2.0BAE Tracker
Cornell DatabaseISI Diffusion
Diffusion over Sensoria Radio
iPAQ802.11
ISI EastDisplay
Laptop802.11
Sensoria Wins2.0Gateway
Cornell DatabaseISI Diffusion
EthernetSensoria: Wins2.0 hardware, radio.
BAE Austin: Multi-modal signal processingdetection, and Kalman tracker.
Cornell: Distributed database query.
ISI West: Directed diffusion
ISI East: Grass display, 802.11 interface.
Team Members: Responsibilities
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 20
ScientificTeam forOperationalMulti-Processors
Working in the Santa Fe hotel roompreparing for the real-time wireless demo.
Carl, Brian, Johannes, Joe, Jorgen, and Manuel.Not shown are Steve (taking the pic) and Fabio.
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 21
Tracking Results
3.3501 3.3502 3.3502 3.3502 3.3502 3.3502 3.3503 3.3503 3.3503 3.3503 3.3503
x 106
6.2848
6.285
6.2852
6.2854
6.2856
6.2858
6.286x 10
5
Easting (m)
Nor
thin
g (m
)
++
Measurement
Smoothed Value
Prediction
Target Moving Southwest
January 13, 2002Austin Test SiteLake Road
Southwest Vehicle Runs
Kalman Trial 1 Trial 2 GT Speed 15 mph 15 mphNode 35 18 18Node 36 18 17Node 37 18 18Node 38 11 11
Errors primarily due to GPS node position errors, and driver speed errors.
Real-time Kalman Tracker Results
DDF Information Tracker Results
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 22
Target Moving NortheastTracking Results
January 13, 2002Austin Test SiteLake Road
Northeast Vehicle Runs
Kalman Trial 1 Trial 2 GT Speed 15 mph 15 mphNode 37 13 13Node 36 20 23Node 35 17 12Node 34 18 18
3.3502 3.3502 3.3502 3.3502 3.3502 3.3503 3.3503 3.3503 3.3503 3.3503 3.3504
x 106
6.2848
6.285
6.2852
6.2854
6.2856
6.2858
6.286
6.2862x 10
5
++
Measurement
Smoothed Value
Prediction
Easting (m)
Nor
thin
g (m
)
Errors primarily due to GPS node position errors, and driver speed errors. DDF Information Tracker Results
Real-time Kalman Tracker Results
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 23
Information Filter*
• Maintains the same information as a Kalman filter, but in inverse covariance form
• Update in response to new information is much simpler• Y(k+1|k+1) = Y(k+1|k)+I(k+1)•
• Prediction and state estimation are more complicated
• In distributed data fusion with many nodes, information update is needed much more often than prediction or state estimation
)1k(i)k|1k(y)1k|1k(y
* Decentralized Data FusionProf Hugh Durrant-WhyteUniversity of Sydney, Australia
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 24
On-Going CSIP Development and Testing
On-Going CSIP Development and Testing
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 25
API Services for Distributed Sensor Networks
Operational Functionality and Support• Low level signal processing.• Repositories for TS, SP, and HL data and information.• Robust adaptive detection for multi-modal sensors.• Decentralized Kalman tracker.• Simple target classifier.• Event logging and post analysis
Developmental Functionality and Support• Bayesian conditional processing.• Power Aware detection, Rational agent classifier.• Localization using array bearing estimation.• PMHT and DIF tracking.• Tactical query decision support.
IntelligentSurveillanceandReconnaissance
Tactical SituationAwareness
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 26
BAE Field Test and Demonstration Sites
• Tracking Environment / Scenarios
• Unconstrained
• Intersection
• Linear
• Field Configuration
• Sensing Distance
• Node Placement
• Path Options
• Sensing Environment
• Grassland
• Tree Cover
• Roadway
• Building Proximity
• Building Interior
• Radio & GPS Environment
• Grassland
• Tree Cover
• Roadway
• Building Proximity
• Building Interior
BAE Austin is starting a series ofsensing and tracking exercises.
These are targeted at situations notencountered at 29 Palms.
They will force evaluation of sensors,algorithms, and systems.
Addresses many of Jim Reich’s Challenge Problems
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 27
BAE Austin Field Test Site - Aerial
Open Roadway
• Linear Approach
• Intersection
• Open Environment
• Quiet Seismic
Building Alcove
• Flexible Approach
• Low Buildings
• People and Vehicles
Long Roadway• Extended Tracking Time
• Intersection
• Tree Line / Open Margin
Test Field• Unconstrained Approach
• Flexible Lay-down
• Grassland
• Brush
Tree grove• Flexible Placement
• Flexible Approach
• Pavement and Grass Surface
Hallways
• Interior Environment
• Limited Exposure
• People
BAE SystemsAustin, TX
Sensor Agent Processing Software (SAPS)
Santa Fe, NM.Collaborative Signal Processing, Jan. 16, 2002 28
Node Setup Along the Road