sensor emplacement network analysis for detection of cross border tunnels ltcol oscar garcia-olalla...

Sensor Emplacement Network Analysis for Detection of Cross Border Tunnels

LtCol Oscar Garcia-Olalla

MAJ Manuel A. Ugarte

2 November 2011

Purpose and Agenda

Agenda• Background.• Objective, Issues, & Scope.• Constraints, Limitations, & Assumptions.• Threat Analysis.• SENTRIES* model

– Sensor Emplacement Network for Tunnel Reconnaissance & Interdiction Emulation Simulation

• Summary.• Questions.

2 November 2011

To provide the results of the sensor emplacement network study in support of a tunnel defeat effort.

2Tunnel Sensor Emplacement Analysis

*SENTRIES - a soldier stationed at a place to stand guard and prevent the passage of unauthorized persons, especially a sentinel stationed at a pass, gate, opening in a defense work, or the like.

Area of Interest

Area of Research Focus

Study Objective, Issues, & Scope

• Objective.– Determine the sensor emplacement network that maximizes the probability of

detecting the construction of tunnels along the U.S. border according to a selected geographic location.

• Issues.– Issue 1: What is the combination, localization and array of persistent

underground sensors to maximize the probability of detection within a given area of interest?

– Issue 2: What is the mix of sensors for border area coverage that provides the greatest probability of detecting cross-border tunnel activity?

– Issue 3: What is the life cost estimate of the sensor packages for the tunnel activity risks areas?

• Scope.– This research coalesces exploratory network simulation analysis to gain

insights concerning sensor allocation, configuration, placement, and prioritization of sensor field emplacement along the U.S. southern border.

2 November 2011 4Tunnel Sensor Emplacement Analysis

Nogales GIS Data Analysis


01

02

03

04

0

Number of tunnels, by sophistication level

Sophistication

0 1-2 3-4

OtherNogales

• Outside the drain network we see both “short-and-shallow” tunnels and the more sophisticated type.

Nogales presents a unique set of challenges because of its network of drains.


Abstract Network Overlay (Top View)

100 m

50 m


- seismic sensors

Ground Surface

House

Shallow(approx 10m)

Deep(approx 20m)

Cij

PdijNode

i

Nodej

Our Target: Tunneling Activity

SENTRIES’ goal is to simulate tunneling activity & localize the sensors in the most likely nodes.

US-MX C

ross

Bor

der F

enceWarehouse

Exit Node

T

Entry Node

S

HouseHouse

Algorithms – Searching safer path (less risk)

min

s.t. NETFLOW constraints


(i,j) ε E

(i,j) ε E

Prob of det of arc (i,j)

Prob of det of a sensor in node i

Sensor in node i [0,1] Flow on arc (i,j) [0,1]

Cost of arc (i,j)

Algorithms – Searching where to place Sensors and re-solving for a safer path


• Solving using MAXIMIZING Z (Dual Objetive Function), we find the best placement of sensor.

• Finally, now that we have computed sensor locations, we re-solve for the operator's new best tunneling route.

Shallow level Deep level

TSensor POD = .7 Sensor POD = .35

USShRe1 2

.022

3 .05

USShRe2 2

.016

3 .05

USShRe3 4

.016

3 .05

USShRe4 3

.011

3 .05

USShRe5 4

.022

4 .042

USDeRe1 5 .036

USDeRe2 5 .036

USDeRe3 5 .036

USDeRe4 5 .036

USDeRe5

3 .05

3 .05

3 .05

3 .05

3 .05

5 5 5 5 5

USShMi1 6 .1

3 .05

USShMi2 6

.095

3 .05

USShMi3 5

.089

3 .05

USShMi4 7

.11

3 .05

USShMi5 6 .1

4 .042

USDeMi1 5 .036

USDeMi2 5 .036

USDeMi3 5 .036

USDeMi4 5 .036

USDeMi5

3 .05

3 .05

3 .05

3 .05

3 .05

5 5 5 5 5

USShFr1 12

.224

3 .05

USShFr2 100 .999

3 .05

USShFr3 11

.196

3 .05

USShFr4 9

.14

3 .05

USShFr5 8

.14

4 .042

USDeFr1 5 .036

USDeFr2 5 .036

USDeFr3 5 .036

USDeFr4 5 .036

USDeFr5

2.2 .031

2.2 .031

2.2 .031

2.2 .031

2.2 .031

3.5 .028

3.5 .028

3.5 .028

3.5 .028

3.5 .028

MxShFr1 6

.112

1.5 .025

MxShFr2 11

.224

1.5 .025

MxShFr3 100 .999

1.5 .025

MxShFr4 9

.196

1.5 .025

MxShFr5 9

.196

2 .022

MxDeFr1 2.5 .017

MxDeFr2 2.5 .017

MxDeFr3 2.5 .017

MxDeFr4 2.5 .017

MxDeFr5

1.5 .025

1.5 .025

1.5 .025

1.5 .025

1.5 .025

2.5 .017

2.5 .017

2.5 .017

2.5 .017

2.5 .017

MxShMi1 3

.084

1.5 .025

MxShMi2 5

.084

1.5 .025

MxShMi3 8

.168

1.5 .025

MxShMi4 5

.084

1.5 .025

MxShMi5 4

.056

2 .022

MxDeMi1 2.5 .017

MxDeMi2 2.5 .017

MxDeMi3 2.5 .017

MxDeMi4 2.5 .017

MxDeMi5

1.5 .025

1.5 .025

1.5 .025

1.5 .025

1.5 .025

2.5 .017

2.5 .017

2.5 .017

2.5 .017

2.5 .017

MxShRe1 2

.056

1.5 .025

MxShRe2 2

.028

1.5 .025

MxShRe3 3

.056

1.5 .025

MxShRe4 1

.028

1.5 .025

MxShRe5 2

.028

2 .022

MxDeRe1 2.5 .017

MxDeRe2 2.5 .017

MxDeRe3 2.5 .017

MxDeRe4 2.5 .017

MxDeRe5

S


US Border

(Shallow/ Deep)- Rear- Middle- Front

MX Border

(Shallow/ Deep)- Front- Middle- Rear

Node and Arcs Map OverviewD

irect

ion

of

Mo

vem

en

t

No Sensors(Unlimited Budget)



T

USShRe1 USShRe2 USShRe3 USShRe4 USShRe5 USDeRe1 USDeRe2 USDeRe3 USDeRe4 USDeRe5

USShMi1 USShMi2 USShMi3 USShMi4 USShMi5 USDeMi1 USDeMi2 USDeMi3 USDeMi4 USDeMi5

USShFr1 USShFr2 USShFr3 USShFr4 USShFr5 USDeFr1 USDeFr2 USDeFr3 USDeFr4 USDeFr5

MxShFr1 MxShFr2 MxShFr3 MxShFr4 MxShFr5 MxDeFr1 MxDeFr2 MxDeFr3 MxDeFr4 MxDeFr5

MxShMi1 MxShMi2 MxShMi3 MxShMi4 MxShMi5 MxDeMi1 MxDeMi2 MxDeMi3 MxDeMi4 MxDeMi5

MxShRe1 MxShRe2 MxShRe3 MxShRe4 MxShRe5 MxDeRe1 MxDeRe2 MxDeRe3 MxDeRe4 MxDeRe5

S 0 Sensors POS = 0.798 Cost = 15.2

1 Sensor (Unlimited Budget)



T







S 1 Sensor POS = 0.794 Cost = 15.2

2 Sensors(Unlimited Budget)



T











T








0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 300

5

10

15

20

25

30

35

40

Tunnel Cost vs. Number of Sensors

# of Sensors

Tu

nn

el

Co

st

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 300

0.10.20.3

0.40.5

0.60.70.80.9 0.80

0.74

0.33 0.32

0.070.06

Operator Resilence Curve

# of Sensors

Op

era

tor

PO

STunnel Defeat Simulation Model

Results

17

• As expected, POS is affected by the number of sensors.

• After 9 sensors, the operator is forced to cross a node with a sensor.

2 November 201117

Tunnel Sensor Emplacement Analysis

• Costs consistently fluctuates in an increasing fashion.

• Number of sensors is not strictly proportional to the Costs.

− Pd in each arcij is not strictly related to the Costs in arcij

Operator Unlimited Budget




T











T







S 13 Sensors POS = 0.324 Cost = 30




T








Tunnel Defeat Simulation Model Results

14-15 October 2009 23CBP Analysis Support

A budget lower that the operator’s Critical Budget will negatively impact the POS.


30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 130

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

8 Sensors (fixed quantity)

Budget

Op

era

tor

PO

S

32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 150

0.05

0.1

0.15

0.2

0.25

0.3

0.35

12 Sensors (fixed quantity)

Budget

Op

era

tor

PO

SCritical Budget Critical Budget

Summary and Way Ahead

Conclusion:– Operator consistently selects less risky routes (based on a limited budget).

– The model provides a quantifiable method to estimate the operator’s critical budget.

– This model provides the sensible number of sensors required to drastically reduced the operator’s POS.

Way Ahead:– Conduct sensor tests under various geological conditions on selected high-

risk areas along the southern border.

– Populate the tunnel detection model with sensor performance data for evaluation.

– Expand the SENTRIES model to incorporate cultural, geographical data to enhance the representation of threat’s behavior.

2 November 2011 24

Tunnels are a threat to national security— this effort will provide CBP with a framework and strategy that will result in an efficient and

effective resource allocation policy.Tunnel Sensor Emplacement Analysis

Questions and Discussion

Cross Border Tunnel Detection and

Sensor Emplacement Network Analysis

BACKUPS


Tunnel Defeat Simulation Model

Simulation Model Incorporates:• Creation of new tunnels• Sensor performance Two Tunnel types:• Shallow• DeepMeasures of Performance:• Probability of Detection vs.

Probability of Success • Tunnel Cost (monetary)

Research Issue: What is the localization, combination, and array of persistent underground sensors to maximize the probability of detection within a given area of interest?

2 November 2011 27

• SENTRIES is flexible and data-driven model that can be easily modified in a relative short time.

• No off-the-shelf models were found that provided the ability to create/ detect tunnels and represent tunnel activity.

Tunnel Sensor Emplacement Analysis

Sensor EmplacementNetwork for Tunnel

Reconnaissance & Interdiction Emulation Simulation

(SENTRIES Model)

Inputs Outputs

• Number existing nodesand respective arcs • Sensor types and locations

• Tunnel Interdiction Plan: routes of intercepted new

tunnels(Operator’s likely tunnel routes)

• Sensors localization plan Localization of nodes for

sensor emplacement plan

Buried Sensors

SurfaceMicrophone

Analysis OPS Center (AOC)In C2 Trailer

Buried Trunk Cable

Road

Buried Sensors

SurfaceMicrophone

Analysis OPS Center (AOC)In C2 Trailer

Buried Trunk Cable

Road

Our Target: Tunneling Activity SENTRIES’ goal is to simulate tunneling activity & localize the sensors in the most likely nodes

2/26

US- MX C

ross

Bord

er F

ence

Deep Tunnels (approx 20

meters below surface)

Shallow Tunnels

(approx 10 meters below surface)

2 November 2011 Tunnel Sensor Emplacement Analysis 29

Scenario Characteristics

Categories Shallow Deep

Traffic Type Human & Marihuana

TrafficCocaine Traffic

Depth from Surface 40 m 80 m

Sensor Pd 0.8 .60

Construction Budget Cost Approx $500k Approx $1million


Probability of Success vs. Number of Sensors


Tunnel Cost vs. Number of Sensors


Budget = 40

Probability of Success vs. Budget


8 Sensors fixed

Probability of Success vs. Budget


12 Sensors fixed

No Sensors



T








1 Sensor



T







S 1 Sensor POS = 0.794 Cost = 15.2

2 Sensors



T








3 Sensors



T








4 Sensors



T








8 Sensors



T








9 Sensors



T








10 Sensors



T








sensor emplacement network analysis for detection of cross border tunnels ltcol oscar garcia-olalla...

Documents