Dr. Joseph R. Guerci Director DARPA/SPO [email protected]/eventsInfo/reviews/2006/slides/May2006/C4IReview-Guerci-2.pdfexample (White Sands DARPA Mountain Top Radar) Sample

1

Advanced Multidisciplinary System Engineeringor

“How I learned to think outside of MY box!”

Dr. Joseph R. GuerciDirector

DARPA/[email protected]

All material cleared for Public Release

2

Outline

• Breakthrough systems/technologies are almost always multidisciplinary– Arise from cross-fertilization– “Cross-fertilization” occurs in someone’s mind

• “Thinking outside the box” = “Thinking outside your box”– Examples:

• KASSPER• HISS

• New Trend in Multidisciplinary Systems Engineering– Level 1: System = Interconnected set of single-purpose subsystems– Level 2: System = Interconnected set of multi-purpose subsystems– Level 3: System = Embedded multi-purpose subsystems w/o clear

boundaries

• Example: ISIS• Summary

Sample SPO Projects(A Multidisciplinary Systems Technology Office)

PRODUCTIAR

IRSGPRODUCT

IDAPathogen DNA DNA Polymerase

5’3’

5’3’

5’3’

5’3’

PRODUCT

Pathogen RNA

Nicking Enzyme

RNAPolymerase

RNAPolymerase

ToxinPRODUCTIAR

IRSGPRODUCT

IDAPathogen DNA DNA Polymerase

5’3’

5’3’ 5’3’

5’3’

5’3’

PRODUCT

Pathogen RNA

Nicking Enzyme

RNAPolymerase

RNAPolymerase

Toxin

Next Generation Chem/Bio Sensors & Protection

Advanced Intelligent Signal Processing &Embedded Systems

Revolutionary Space and Near-Space Technologies

3

mD Adaptive Signal Processing

• Example: Space-Time Adaptive Processing (STAP)

Space-Time Adaptive Beamformer

“Ideal” Adapted Pattern

Optimum Solution

• Weiner-Hopf

1R−=w s(Optimum space-time beamformer weights)(Desired signal “steering vector”)

(Inverse of total interference covariance matrix)

, NM

NM NM

CR C ×

∈

∈

w s

~ 10' 100'NM s s−

4

Covariance Estimation Problem

• Practical implementation example and real data example (White Sands DARPA Mountain Top Radar)

Sample Covariance Estimation Measured Data

ˆi i

iR

∈Ω

′= ∑x x

Ideal (Stationary) Data

• Heterogeneous Clutter– Rapidly varying terrain

• Mountainous (rapid elevation/reflectivity variation)• Rapid land cover variations (e.g., littoral)

• Dense “Target” Backgrounds– “Moving Clutter”

• Military/civilian vehicles

• Large Discretes and “Spiky” Clutter– Urban clutter– Power lines, towers, steep mountainous terrain

• Range-Varying (Nonstationary) Clutter Loci– Bi/Multistatics– Nonlinear array geometries (e.g., circular arrays)

Welcome to the Real-World!

Extremely suboptimal radar performance can occur if one or more of the following occurs: (High false alarm rates and/or low Pd)

One or More of the Above is Almost Always Present in Real-World Ops!

5

Serious Performance Impacts!!(KASSPER ’02 Data Cube & APTI Data Set)

x distance (km) −> (longitude)

y d

ista

nc

e (

km

) −

> (

lati

tud

e) Rx

(35.73°,118.5°)0 20 40 60 80 100

0

10

20

30

40

50

Doppler (Fraction PRF)

Rang

e Bi

n #

GMTI Range−Doppler Data (dB−thermal)

−0.5 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3 0.4

10

20

30

40

50

60

−50 0 50 100 150−70

−60

−50

−40

−30

−20

−10

0

SIN

R/S

NR

o (

dB

)

Doppler (m/s)

rang bin 240 (38.6km)

optimalPCI−40MWF−40post−Doppler (3 bin)

SINR Loss High False Alarm Rates

−10 −5 0 5 10 15 20 25 3010

−5

10−4

10−3

10−2

10−1

100

Pixel SINR (dB)

Frac

tion

Exc

eedi

ng V

alue

AMF Exceedance

STAP Only STAP w/ Pre−Whitening

Radar Environmental Knowledge Bases(DTED/DFAD/LCLU, SAR, etc.)

Knowledge-Aided Sensor Signal Processing & Expert Reasoning (KASSPER)

Clutter Knowledge Base

ICCR iiiKA22

CellsClutter Over Sum

σγ +∑ ′=

iγClutter Cell Returns

GPS/INS

N Elements

[ x1 x2 x3 • • • xM ]

.

.

.

T TM Pulses

. . . .

.

.

T T

M Pulses

Array Snapshots

Sensor Characteristics

iCClutter Steering Vectors


ICCR iiiKA22


σγ +∑ ′=


GPS/INS

N Elements

[ x1 x2 x3 • • • xM ]

.

.

.

T TM Pulses

. . . .

.

.

T T

M Pulses

Array Snapshots


GPS/INS

N Elements

[ x1 x2 x3 • • • xM ]

.

.

.

T TM Pulses

. . . .

.

.

T T

M Pulses

Array Snapshots


GPS/INSGPS/INS

N Elements

[ x1 x2 x3 • • • xM ]

.

.

.

T TM Pulses

. . . .

.

.

T T

M Pulses

Array Snapshots

N Elements

[ x1 x2 x3 • • • xM ]

.

.

.

T TM Pulses

. . . .

.

.

T T

M Pulses

Array Snapshots



-60

-50

-40

-30

-20

-10

0

10

20

-0.5 0 0.5

-0.5

0

0.5-60

-50

-40

-30

-20

-10

0

10

20

-0.5 0 0.5

-0.5

0

0.5

XNonstationary Clutter

(plus Signal)

XRY KA21−=

21−

KAR 21ˆ −

SMIR

Reduced-RankConventional FilterKA Pre-Filter

YRZ SMI21ˆ −=

Detector

1st StageKnowledge-Aided

Pre-Filter Response

2nd StageConventional

Filter

KASSPERClutter Knowledge Base

ICCR iiiKA22


σγ +∑ ′=


GPS/INS

N Elements

[ x1 x2 x3 • • • xM ]

.

.

.

T TM Pulses

. . . .

.

.

T T

M Pulses

Array Snapshots




ICCR iiiKA22


σγ +∑ ′=


GPS/INS

N Elements

[ x1 x2 x3 • • • xM ]

.

.

.

T TM Pulses

. . . .

.

.

T T

M Pulses

Array Snapshots


GPS/INS

N Elements

[ x1 x2 x3 • • • xM ]

.

.

.

T TM Pulses

. . . .

.

.

T T

M Pulses

Array Snapshots


GPS/INSGPS/INS

N Elements

[ x1 x2 x3 • • • xM ]

.

.

.

T TM Pulses

. . . .

.

.

T T

M Pulses

Array Snapshots

N Elements

[ x1 x2 x3 • • • xM ]

.

.

.

T TM Pulses

. . . .

.

.

T T

M Pulses

Array Snapshots



-60

-50

-40

-30

-20

-10

0

10

20

-0.5 0 0.5

-0.5

0

0.5-60

-50

-40

-30

-20

-10

0

10

20

-0.5 0 0.5

-0.5

0

0.5

XNonstationary Clutter

(plus Signal)

XRY KA21−=

21−

KAR 21ˆ −

SMIR

Reduced-RankConventional FilterKA Pre-Filter

YRZ SMI21ˆ −=

Detector

1st StageKnowledge-Aided

Pre-Filter Response

2nd StageConventional

Filter

KASSPER

Measured(DARPA Mtn Top)

Predicted(DTED Level-1)

Ran

ge

Doppler

Bald Earth

1980

Physical

20001980

HPEC

Real-TimeDatabase

EM ModelingTools

PhysicalDatabases

6

CACFARAGC, etc.

IF SidelobeCanceler

Fully AdaptiveArray

Space-TimeAdaptive (STAP)

Radar

Advanced and Real-Time

STAP50’s 60’s 70’s 80’s 90’s

Reinventing Adaptive Radar

First Gen Statistical Signal Processing

KASSPER

Intelligent Adaptive Radars“Real-world nonstationarity does NOT

support conventional adaptivity”

00’s 10’s

Real-time

knowledge-aided

KASSPER

Classic

Savant

FLOPS/Throughput

Knowledge

Data type/MBytes

High-speed, single function

Multi-function, slow access

speeds

True “Intelligent” Processing

+

SAR Roads VMAP Discrete

Radar returns

Old

New

∑ ′=Ω

kkiR xxˆ

Space-TimeSnapshot

Vector

RangeCells

Test Cell

“Guard”Cells

2

1

1

2

−

−

+

+

i

i

i

i

i

xxxxx

...

...

Ω

Ω

ConventionalSpace-Time Filtering

sw 1−= R

QR Factorization w/ Back substitution(from Antenna-Based Signal Processing Techniques for Radar, A. Farina, Artech House)

Highly Parallel Systolic Array Implementation(Achieves 100’s to 1000’s of GFLOPS)

KASSPER HPEC Challenge:Optimizing adaptation by injecting environmental knowledge “intelligently” into the front-end signal flow

First Gen Real-Time KASSPER HPEC

ClutterKnowledge

Base

IntelligentSignal

Processing

• KASSPER requires memory access interrupts

• Optimal interrupt scheduling

• Optimized ISP• “Look-Ahead” scheduling

• KASSPER requires memory access interrupts

• Optimal interrupt scheduling

• Optimized ISP• “Look-Ahead” scheduling

Conventional vs. KASSPER HPEC Processing

7

“Look-Ahead” Scheduling Addresses Memory Latency Issues

CPU Registers

Registers

FasterSpeedHigherCost

StagingTransfer Unit

100s Bytes<1s ns

Cache10s-100s K Bytes1-10 ns

Source: Dave Patterson, Graduate Computer Architecture Course, University of California, Berkeley, Spring, 2001

Disk10s G Bytes10 ms

TapeInfinitesec-min

Main MemoryM Bytes100-300 ns

Cache

Memory

Disk

Tape

Instr. Operands

Blocks

Pages

Files

Prog./Compiler 1-8 Bytes

Cache Controller 8-128 Bytes

OS512-4K Bytes

User/Operator MBytes Larger

SizeLowerCost

CapacityAccess Time

CPU Registers

Registers

FasterSpeedHigherCost

StagingTransfer Unit

100s Bytes<1s ns

Cache10s-100s K Bytes1-10 ns

Source: Dave Patterson, Graduate Computer Architecture Course, University of California, Berkeley, Spring, 2001

Disk10s G Bytes10 ms

TapeInfinitesec-min

Main MemoryM Bytes100-300 ns

Cache

Memory

Disk

Tape

Instr. Operands

Blocks

Pages

Files

Prog./Compiler 1-8 Bytes

Cache Controller 8-128 Bytes

OS512-4K Bytes

User/Operator MBytes Larger

SizeLowerCost

CapacityAccess Time

Problem:KASSPER

“Look-Ahead”Interrupt Scheduling

ttt ∆+

ClutterKnowledge

Base

Predictor

Solution:

Next-Gen KASSPER HPEC Testbed

• Architecture:– Base computer and I/O cards

purchase order completed– Lab computer configuration complete– Various processing concepts in review– PDR planned for late June 03– Demonstration at DARPATech 04

• Parallel Vector Library (PVL) chosen for open standards programming language

– LL reviewing initial KASSPER algorithms for library impacts

– Coding started on basic radar signal processing components (pulse compression, data retrieval, etc.)

– Algorithm developers will program the hardware Vendor

Hardware

PortableLibrary

Maps

ApplicationCode

VendorSoftware

Open standards for real-time processing

MP-510 mercury processing

Multiple high-speed RAID drives

ASIC high-speed cache memory devices

VendorHardware

ApplicationCode

VendorSoftware

• Upgrades restricted to hardware remapping & new features

8

Pre-filtering Followed by Conventional STAP

0

5

10

15

20

25

30


Ran

ge

Bin

#

GMTI AMF Output (dB−thermal)

−0.5 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3 0.4

10

20

30

40

50

60

0

5

10

15

20

25

30


Ran

ge

Bin

#

GMTI AMF Output after Whitening (dB−thermal)

−0.5 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3 0.4

10

20

30

40

50

60

−10 −5 0 5 10 15 20 25 3010

−5

10−4

10−3

10−2

10−1

100

Pixel SINR (dB)

Fra

ctio

n E

xcee

din

g V

alu

e

AMF Exceedance

STAP Only STAP w/ Pre−Whitening

Adaptive Matched Filter

With Prefiltering

Without Prefiltering

Better Behaved “Tail”

Pre-Filtering Reduces The “Tail” of the Exceedance Function

13 dB!

KASSPER:“It’s an Architecture, NOT an Algorithm”

KASSPER is an architecture for real-time adaptation of multidimensional sensor systems in real-world environments

KASSPER is an architecture for real-time adaptation of multidimensional sensor systems in real-world environments

• KASSPER Architecture– Environmental context is key to efficient adaptation

• Sensors, like humans, benefit from context!– Key enablers: “look-ahead” scheduling and resource allocation – Multiresolution philosophy: blurring the boundaries between SAR

and GMTI– KASSPER as a modern manifestation of the “Bayesian” method!

• KA-STAP Bayesian STAP• The DARPA KASSPER Challenge: Creatively explore the

possibilities– Re-examine entire adaptive signal processing paradigm with an eye

towards maximizing knowledge-aided “robust” methods– Robust STAP algorithms AND KASSPER architecture

• Environmental knowledge base as “read/write” scratch memory– What is “implementable”? 2010? 2020?– Environmentally aware sensors have a future!

• KASSPER Architecture– Environmental context is key to efficient adaptation

• Sensors, like humans, benefit from context!– Key enablers: “look-ahead” scheduling and resource allocation – Multiresolution philosophy: blurring the boundaries between SAR

and GMTI– KASSPER as a modern manifestation of the “Bayesian” method!

• KA-STAP Bayesian STAP• The DARPA KASSPER Challenge: Creatively explore the

possibilities– Re-examine entire adaptive signal processing paradigm with an eye

towards maximizing knowledge-aided “robust” methods– Robust STAP algorithms AND KASSPER architecture

• Environmental knowledge base as “read/write” scratch memory– What is “implementable”? 2010? 2020?– Environmentally aware sensors have a future!

9

Emerging Field

• Special Issue of IEEE Signal Processing Magazine

Handheld Isothermal Silver Standard Sensor(HISSS)

The goal of the HISSS program is to develop a handheld sensor that is capable of identifying biological threats including bacteria, viruses and toxins.

Polymerase Chain Reaction (PCR) Machine Notional Sensor

DNA detection

RNA detection

Protein detection

Fluid handling

DNA readout

RNA readoutProtein readout

System check

Notional Sample Cartridge

How to shrink into a handheld?• Order-of-mag faster!• At least as accurate!

10

PCR vs. Isothermal

∆t ~ 60 sec

Anneal at 55ºC

Starting the process:Primers PolymerasePathogen DNA 5’

3’

Products: copies of Pathogen DNA5’3’

Extendat 72ºC

Denature at 95ºC

5’

5’3’

3’

3’

5’5’

3’5’5’

3’

5’

5’3’

Polymerase Chain Reaction

3’ 5’

3’ 5’

3’

Cleave5’3’

Product falls off5’3’

Polymerase re-binds

5’3’

5’

Extend

3’

Products: copies of reporter

Nicking enzyme

Starting the process:

Polymerase

Trigger template

Pathogen DNAIsothermal

∆t ~ 3 sec

HISS DNA Amplification

11

HISSS Progress

• Progress:– Demonstrated false alarm rates, using ROC curve analysis for HISSS assays that

are equal to or better than current DNA, RNA, and protein assays– Successfully developed and utilized a flow-through testbed to test all assays

0

0.2

0.4

0.6

0.8

1

0.01 0.1 10.10.01 1Pfa

DNA ROC Curves

0.8

0.6

0.4

0.2

0.0

1.0

Pd

PI (1:99)PII (1:99)PCR (1:99)

0

0.2

0.4

0.6

0.8

1

0.01 0.1 10.10.01 1Pfa

DNA ROC Curves

0.8

0.6

0.4

0.2

0.0

1.0

Pd

PI (1:99)PII (1:99)PCR (1:99)

PI (1:99)PII (1:99)PCR (1:99)

0

0.2

0.4

0.6

0.8

1

0.001 0.01 0.1 10.001 0.10.01 1

0.8

0.6

0.4

0.2

0.0

1.0

Pd

Pfa

RNA ROC Curves

PI (1:82)PII (1:82)RT-PCR (1:82)

0

0.2

0.4

0.6

0.8

1

0.001 0.01 0.1 10.001 0.10.01 1

0.8

0.6

0.4

0.2

0.0

1.0

Pd

Pfa

RNA ROC Curves

PI (1:82)PII (1:82)RT-PCR (1:82)

PI (1:82)PII (1:82)RT-PCR (1:82)

0

0.2

0.4

0.6

0.8

1

0.001 0.01 0.1 1

Pd

0.01 10.10.001Pfa

0.8

0.6

0.4

0.2

0.0

1.0Protein Toxin ROC Curves

PI (1:3000)PII (1:3000)ELISA (1:3000)

0

0.2

0.4

0.6

0.8

1

0.001 0.01 0.1 1

Pd

0.01 10.10.001Pfa

0.8

0.6

0.4

0.2

0.0

1.0Protein Toxin ROC Curves

PI (1:3000)PII (1:3000)ELISA (1:3000)

PI (1:3000)PII (1:3000)ELISA (1:3000)

PI Static (1:99)PII Flow (1:99) PCR (1:99)

PI Static (1:82)PII Flow (1:82) RT-PCR (1:82)

PI Static (1:3000)PII Flow (1:3000) ELISA (1:3000)

New Airship Design Philosophy

MDA Airship

Payload bay

Conventional Airship

Capability cannot be added to airship after development

Payload: ~2% of system mass

ISIS requires integration of sensor and airship

Payload: 30-40% of system mass

Turn a disadvantage (large size) into an advantage (large antenna)!

12

The “First” ISIS?

Echo 1

Simultaneous AMTI/GMTI Operation via Dual Band (UHF/X-Band) Aperture

Most Powerful Airborne GMTI/AMTI Radar & Comms Ever Conceived

Long-range AMTI/GMTI/COMM

FOPEN GMTI

Cruise Missile Defense

Steep Grazing AnglesDetect/Track Dismounts

Extremely High Capacity CommsNear Zero Platform Speed

No In-Theater Ground Support – 99% on station availability for 1+ years600km radar horizon at 70kft operational altitude

13

ISIS

Joint STARSJoint STARS

AWACSAWACS

Global HawkGlobal HawkGlobal Hawk

109108107106105104103102100 101 109108107106105104103102100 101

160,000300,000,000

1.0 Relative Search Capability (PA/R2)1.0 Relative Track Capability (PA2/λ2/R4)

2405,100

3,30015,000

ISAT 140610

VHFX

SS

XX

XX

Unprecedented Radar Performance

Platform Carries the Antenna

Antenna Is the Platform

Sustained Operations Logistics

• Aircraft-based ISR Requires– Local air base– Multiple aircraft to keep 1 flying – Air crews– Ground crews– Fuel supplies– Maintenance facilities

• ISIS – Unmanned– Deploys worldwide from U.S.

base– Regenerative Fuel Sources– One-year continuous ISR

capability

14

Wind Conditions DrivePropulsion Power Needs

ηρ

2

33/2 vVCP d⋅

=Where ρ = air density at altitude

V = volume of airshipv = relative velocity of airη = efficiency of propellers

Propulsion Power for V = 106 m3

(Cd=0.022)

0500

1,0001,5002,0002,500

0 10 20 30 40 50

Wind Speed (m/sec)

Pow

er R

equi

red

(kw

)

Propulsion Power for V = 106 m3

(Cd=0.022)

0500

1,0001,5002,0002,500

0 10 20 30 40 50

Wind Speed (m/sec)

Pow

er R

equi

red

(kw

)

44.95 m/s

Max winds drive powersystem requirements

Station Keeping

• ISIS Objective: 99% on-station availability for 1 year– Function of airship speed (sustained and sprint) and available

energy (regenerative and stored fuel)

• Need operational algorithms for maximizing availability– Managing airship energy ala satellite delta-v

-90-80-70-60-50-40-30-20-10

0102030405060708090

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75

Wind Speed (m/s)

Latit

ude

(deg

ress

)

Mean Wind Speed Average 99 PercentileMean Wind Speed Average 99 Percentile

Maxim

um S

print Speed

15

Requires Large Mass Reductions

Mass

VolumePo

wer

Mass

VolumePo

wer

MV ∝

3/2VP ∝

PM ∝ • ISIS designs are mass-centric– Lifting gas has reached the maximum limit: – 0.061kg per 1m3 of He @ 21km– 0.066kg per 1m3 of H2 @ 21km

• ISIS focusing on:– Removing mass from largest contributors– Integration, INTEGRATION, INTEGRATION!

avionicspropulsionpropulsion

dairradar

power

poweraperturehullhgasISIS

avionicspropulsionpowerradarstructuregasliftingairdisplaced

MMvVCPAVcVM

MMMMMMM

++⎟⎟⎠

⎞⎜⎜⎝

⎛++++=

+++++=

ηρ

ηρ

ρρρ2

33/23/2

Integration

Components

Summary

• Breakthrough systems/technologies are almost always multidisciplinary

– System engineers need to be continually learning about new technologies and methods across ALL disciplines

• “Be an annoying know-it-all!”– Tactic: “Can the thermal engineer give the flight control engineer’s

briefing?”– Often “cross fertilization” can occur even if with only a 1st or 2nd

order understanding of multiple disciplines• Balance of depth and breadth

– How should engineering programs be structured in light of above?• Undergraduate programs typically have the breadth, but don’t seem to

“close the deal”– Example: Senior class semester devoted to dissecting a complex system

• Emergence of a “Level 3” systems integration– Multidisciplinary from its inception!

Dr. Joseph R. Guerci Director DARPA/SPO [email protected]/eventsInfo/reviews/2006/slides/May2006/C4IReview-Guerci-2.pdfexample (White Sands DARPA Mountain Top Radar) Sample

Documents