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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

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Strategies for multi asset surveillance

Strategies for Multi-Asset Surveillance

Dr. William M. Spears

Dimitri Zarzhitsky

Suranga Hettiarachchi

Wesley Kerr

University of Wyoming


Scenario
Scenario

Target detector

Foliage detector

Maximize the number of T targets found by α assets.


Forest generator
Forest Generator

L x L environment

with T targets

and foliage.


Asset control
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).


Target control
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.


Stationary targets
Stationary Targets

Why is SLAF so poor and SSLAF so good?


Asset coverage maps
Asset Coverage Maps

SL

SLAF

SSLAF

SL provides uniform coverage of the space. SSLAF provides increased

uniform coverage of the non-foliage space. But SLAF misses entire regions.


Gedanken experiment
Gedanken Experiment

What if the targets move slowly from left to right? Will the prior results change?


Gollum targets
Gollum Targets

Why is SLAF so good?


Probabilistic analysis
Probabilistic Analysis

Controller 3:

Uniformly cover

one diagonal (average case SLAF).

Controller 1:

Uniformly cover

whole area (like SL).

Controller 2:

Uniformly cover

one column (best

case SLAF).

Controller 4:

Uniformly cover

one row (worst case

SLAF).


Area controller
Area Controller

Visibility time

of target.

Expected number of time

steps for asset to cover area.





Comparison of controllers
Comparison of Controllers

SLAF works well on moving targets

because diagonal controller performance

is like column controller performance.


Summary
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.


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