1 / 16

Strategies for Multi-Asset Surveillance

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

cheyenne-bass
Télécharger la présentation

Strategies for Multi-Asset Surveillance

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Strategies for Multi-Asset Surveillance Dr. William M. Spears Dimitri Zarzhitsky Suranga Hettiarachchi Wesley Kerr University of Wyoming

  2. Scenario Target detector Foliage detector Maximize the number of T targets found by α assets.

  3. Forest Generator L x L environment with T targets and foliage.

  4. 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).

  5. 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.

  6. Stationary Targets Why is SLAF so poor and SSLAF so good?

  7. 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.

  8. Gedanken Experiment What if the targets move slowly from left to right? Will the prior results change?

  9. Gollum Targets Why is SLAF so good?

  10. 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).

  11. Area Controller Visibility time of target. Expected number of time steps for asset to cover area.

  12. Column Controller

  13. Diagonal Controller

  14. Row Controller

  15. Comparison of Controllers SLAF works well on moving targets because diagonal controller performance is like column controller performance.

  16. 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.

More Related