bae systems austin, tx sensor agent processing software (saps) santa fe, nm.collaborative signal...

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




Presentation Overview

• Accomplishments

• Collaborative System Architecture

• Sensors and Signal Analysis

• Collaborative Processing

• On-Going Work




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




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




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.




CollaborativeSystem

Architecture




Node Software Architecture• Robust adaptive multi-modal sensor data processing.• Five repositories to support collaboration, dynamic situation awareness, and decentralized data fusion.




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




Sensors and Signal AnalysisSensors and

Signal Analysis




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




InputDatax[10]

TargetModelFeatureExtraction

BackgroundModel Features

LogLikelihoodRatioTest

DetectionThresholds

ScoreNormalization

NormalizationParameters

Confidence

Binary Detection

Time Stamp

DetectionLatencyHeuristics

AmplitudeZero CrossingPolarity Direction

PIR Detection Processing




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




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



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



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




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




- 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.




Collaborative Signal and

Information Processing

Collaborative Signal and

Information Processing




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




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




Tracking System

Sensoria Wins2.0BAE Tracker

Cornell DatabaseISI Diffusion









Diffusion over Sensoria Radio

iPAQ802.11

ISI EastDisplay

Laptop802.11

Sensoria Wins2.0Gateway


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




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.




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




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




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




On-Going CSIP Development and Testing

On-Going CSIP Development and Testing




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 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 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




Node Setup Along the Road

bae systems austin, tx sensor agent processing software (saps) santa fe, nm.collaborative signal...

Documents