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Strategies for Multi-Asset Surveillance. Dr. William M. Spears Dimitri Zarzhitsky Suranga Hettiarachchi Wesley Kerr University of Wyoming. Scenario. Target detector. Foliage detector. Maximize the number of T targets found by α assets. Forest Generator. L x L environment - PowerPoint PPT Presentation
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UW Computer Science DepartmentUW Computer Science Department
Strategies for Multi-Asset Surveillance
Dr. William M. Spears
Dimitri Zarzhitsky
Suranga Hettiarachchi
Wesley Kerr
University of Wyoming
UW Computer Science DepartmentUW Computer Science Department
Scenario
Foliage detector
Target detector
Maximize the number of T targets found by α assets.
UW Computer Science DepartmentUW Computer Science Department
Forest Generator
L x L environmentwith T targetsand foliage.
UW Computer Science DepartmentUW Computer Science Department
Asset Control
• Behavior-based asset controllers.– Straight Line (SL)
• Assets “bounce” off boundary walls. Ignores foliage.
– Straight Line Avoid Forest (SLAF)• Like SL but also reverse course if encounter foliage.
– Super Straight Line Avoid Forest (SSLAF)• Like SLAF but move opposite to center of mass of
foliage (a more sophisticated foliage sensor).
UW Computer Science DepartmentUW Computer Science Department
Target Control
• Stationary targets for baseline study.
• “Hiding Gollum” target controller:– Targets try to cross from left to right through
environment while hiding in foliage.
UW Computer Science DepartmentUW Computer Science Department
Stationary Targets
Why is SLAF so poor and SSLAF so good?
0
20
40
60
% Targets Found
10 20 30 40 50 60 70
% Foliage
Performance on Stationary Targets
SL
SLAF
SSLAF
UW Computer Science DepartmentUW Computer Science Department
Asset Coverage Maps
SL SLAF SSLAF
SL provides uniform coverage of the space. SSLAF provides increaseduniform coverage of the non-foliage space. But SLAF misses entire regions.
UW Computer Science DepartmentUW Computer Science Department
Gedanken Experiment
What if the targets move slowly from left to right? Will the prior results change?
UW Computer Science DepartmentUW Computer Science Department
Gollum Targets
Why is SLAF so good?
0
20
40
60
80
% Targets Found
10 20 30 40 50 60 70
% Foliage
Performance on Gollum Targets
SL
SLAF
SSLAF
UW Computer Science DepartmentUW Computer Science Department
Probabilistic AnalysisController 1:Uniformly coverwhole area (like SL).
Controller 4:Uniformly coverone row (worst case SLAF).
Controller 2:Uniformly coverone column (bestcase SLAF).
Controller 3:Uniformly coverone diagonal (average case SLAF).
UW Computer Science DepartmentUW Computer Science Department
Area Controller
t
t
t
tt
t
S
rv
r
v
v
LS
r
LS
STE
t
2cityasset velo
asseton detector target of radius
ocitytarget vel
assets ofnumber
111found] targets[
2
2
Expected number of timesteps for asset to cover area.
Visibility timeof target.
UW Computer Science DepartmentUW Computer Science Department
Column Controller
t
t
t
t
tt
S
rd
rv
r
v
v
LS
d
LS
STE
t
2thcolumn wid
2cityasset velo
asseton detector target of radius
ocitytarget vel
assets ofnumber
111found] targets[
UW Computer Science DepartmentUW Computer Science Department
Diagonal Controller
t
t
t
t
tt
S
rd
rv
r
v
v
LS
d
LS
STE
t
2thcolumn wid
2cityasset velo
asseton detector target of radius
ocitytarget vel
assets ofnumber
22111found] targets[
UW Computer Science DepartmentUW Computer Science Department
Row Controller
height row2
2found] targets[
t
t
rL
TrE
UW Computer Science DepartmentUW Computer Science Department
Comparison of Controllers
SLAF works well on moving targetsbecause diagonal controller performance is like column controller performance.
Comparison of Controllers
0
0.2
0.4
0.6
0.8
1
1.2
0 .2 .4 .6 .8 1.0 1.2 1.4 1.6 1.8
target velocity
% t
arg
ets
fo
un
d Area Controller
Colum n/DiagonalController
Row Controller
UW Computer Science DepartmentUW Computer Science Department
Summary
• Developing predictive mathematical theory for multiple assets performing surveillance.– Currently includes number of assets, their speed, target
speed, and environment size.
– Working on including probability of detection (a noisy sensor), percentage of foliage, and time limits on mission length.
• Goal is to provide mathematical tools to yield an optimal strategy for a surveillance mission.