1DAT/SENMar 22, 2006

The Junior Woodchucks Guidebook

for Fighter Pilots

Lars Rosenberg RandleffPh.D. student

Danish Defence Research Establishment

2DAT/SENMar 22, 2006

Junior Woodchucks Guidebook

• In Disney's fictional Scrooge McDuck universe, The Junior Woodchucks are the Boy Scout-like youth organization to which Donald Duck's nephews, Huey, Dewey and Louie, belong.

Source: Wikipedia.org

• The nephews always carry with them a copy of the Junior Woodchucks Guidebook, a fictional guidebook which is full of detailed and pertinent information about whatever country or situation in which Donald and the boys find themselves. Its depth of coverage is remarkable, considering that it is a small paperback book.

3DAT/SENMar 22, 2006

Vision for the project

• Numbers of threats, guidance methods and countermeasures are increasing• 600 missile systems developed• 200-300 missile systems currently deployed

(http://missile.index.ne.jp)

• Not easy to find the best action• Takes training to do it in time• Solution depends on mission

• “The worlds best pilot as the co-pilot”

4DAT/SENMar 22, 2006

Missile guidance

Semi-active Radar

Infrared

Electro-Optical

Active Radar

Track-Via-Missile

INS/GPS

Command

Beam Rider

Anti-Radiation

5DAT/SENMar 22, 2006

Data flow

Position

DSS

Situational Awareness

Decision Presentation to the pilot

• Audio?• Display?• Force feedback?

RWR

MWS

Velocity

Orientation

Jammer

Chaff

Flares

Mission data

Link-16 data

Knowledge Base

• AA• SA• Single threat• Multiple threats

Jammer

Chaff

Flares

6DAT/SENMar 22, 2006

Data flow

Position

DSS

Situational Awareness

Decision Presentation to the pilot

• Audio?• Display?• Force feedback?

RWR

MWS

Velocity

Orientation

Jammer

Chaff

Flares

Mission data

Link-16 data

Knowledge Base

• AA• SA• Single threat• Multiple threats

Jammer

Chaff

Flares

7DAT/SENMar 22, 2006

Approaches

• AI techniques• Bayesian Networks• Prolog-based Expert

System• Artificial Neural

Networks

• OR techniques• Mathematical

Modelling• Metaheuristics

8DAT/SENMar 22, 2006

The Bayesian Network

9DAT/SENMar 22, 2006

Dependency tables

• A lot of work to fill them• Not easy without firm knowledge of

all dependencies

• Can be done using HUGIN (Structural Learning)• Not easy to get data

10DAT/SENMar 22, 2006

Using Fly-In

• Uses models to simulate an IR guided missile approach

• Can generate data to be used in Structural Learning

• Has a lot of parameters!

11DAT/SENMar 22, 2006

Looking ahead

• Decisions depend on what to expect

• Maximizing the survivability• Lethality vs. Survivability• What to optimize?

• Defining scenarios• Threats• Countermeasures

12DAT/SENMar 22, 2006

Survivability

tot

iii

iiii

N

iiitot

LS

RRR

MTRLTPL

1

)),,,(1()(

,,

1

n

ttprod

tt

best

n

ttsum

SS

SS

SS

0

0

}{max

13DAT/SENMar 22, 2006

Efficiency of CM (α)

0.2

0.4

0.6

0.8

1

30

210

60

240

90

270

120

300

150

330

180 0

r = getEffAlpha(t, chaff)

0.2

0.4

0.6

0.8

1

30

210

60

240

90

270

120

300

150

330

180 0

r = getEffAlpha(t, jammer)

0.2

0.4

0.6

0.8

1

30

210

60

240

90

270

120

300

150

330

180 0

r = getEffAlpha(t, decoy)

14DAT/SENMar 22, 2006

Threat Lethality (II)

-1 0 1 2 3 4 5

x 104

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4x 10

4

-1 0 1 2 3 4 5

x 104

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4x 10

4

0 50 100 150 200 250 300 350 400 450 5000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Scenario Lethality

15DAT/SENMar 22, 2006

Decision Schedule

Jammer

Decoy

Chaff

16DAT/SENMar 22, 2006

Final remarks

• Difficult to get proper data

• No ground truth to test with

• Work in progress. No “best solution” found yet