Stochastic and deterministic models of targeting, with Dynamic and Error-Prone BDA
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Authors
Gaver, Donald Paul
Subjects
Advisors
Date of Issue
1997-09-01
Date
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
Deep precision strike is a generic military operation that depends importantly on C4/ISR system contributions. Information from the latter is realistically subject to chance influences: targets are found and correctly identified generally at rates proportional to their numbers, locations, and activities, and to the coverage of shooter-serving sensors; the events of detection are realistically random, as are the delays, results, outcomes, and follow-up of the targeting shooters. In this paper a simplified version of the above complicated process is analyzed mathematically, here as a multi-stage queuing process with imperfect service. The probabilistic outcomes can be used to anticipate the results of higher-resolution simulations; these often are far more time consuming both to set up and run. Aspects of the above queuing situations can also be deduced via a deterministic 'fluid' queuing approximation that gives an adequate and convenient representation of aspects of the state variables and various Measures of Effectiveness in the stochastic queuing model. Relying on that agreement, we have elsewhere generalized the stochastic queuing model setup to fluid models that incorporate omitted realities, such as losses from target-list tracking, and the inevitable time dependencies, non- stationarities, and adaptive behaviors that typically occur in actual military operations or vignettes. Both the stochastic and deterministic model results are informative and produce reasonable insights. Further validation steps using mathematical probability techniques as well as simulation are planned; some are in progress.
Type
Technical Report
Description
Series/Report No
Department
Organization
Identifiers
NPS Report Number
NPS-OR-97-018
Sponsors
Prepared for: Space C2 Information Warfare, Strategic Planning Office N6C3, Washington, DC
Funder
Grant No. CNS-0430566 and CNS-0430598.