Development of a Joint Intelligence and Information S&T Capability Task Authorization 22 - Deployable Intelligence Source Collection Value Optimizer (DISCOVER) – Multi-Satellite Collection Scheduling: Closed-Loop Collection Tasking (Multiple Episodes)—Implementation

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Authors
  1. Tharmarasa, R.
  2. Florea, M.
  3. Kirubarajan, T.
Corporate Authors
Defence Research and Development Canada, Valcartier Research Centre, Quebec QC (CAN);Thales Canada, Quebec Que (CAN)
Abstract
Closed-loop collection tasking does explicitly account for information feedback (e.g., observation outcome) interleaving task planning and task plan execution. Environment assumptions are typically characterized by: - uncertainty, - dynamic (on-line) conditional (real-time) task planning (sequential decision), - multiple tasking episodes, and, - a high tempo of operations (state transition rate /events) requiring responsiveness. Dynamic tasking may be appropriate when operating under uncertainty, latency conditions or high operations tempo/state transition rate (e.g., time-dependent communication and decision cycle, observation outcome/feedback, resource availability/failure, urgent task occurrence, delayed observation and information feedback, information-sharing, weather conditions). Through this document, we explore adaptive and contingency planning/scheduling concepts to the dynamic multi-satellite collection scheduling problem. We have also developed a new adaptive scheduling decision problem model properly dealing with state transitions (e.g., exogenous event, new task, feedback information, resource availability/failure, urgent observation requests) while planning and executing tasks and trading-off run-time and solution quality.

Il y a un résumé en français ici.

Keywords
Multi-satellite scheduling
Report Number
DRDC-RDDC-2018-C189 — Contract Report
Date of publication
01 Oct 2018
Number of Pages
40
DSTKIM No
CA047611
CANDIS No
808077
Format(s):
Electronic Document(PDF)

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