A Predictive Task Network Model for Estimating the Effectiveness of Decision Aids for Sonar Operators
Abstract
This project demonstrates an approach to predicting and optimizing the impact of new technologies in system re-design by using simulation to model operator-system functionality. A task network model was developed to create a real- time simulation of the tasks performed by sonar operators in building the underwater picture. This picture is created by analysing sonar data, and the process is made complex by high volumes of noise and multiple data that arrive from a variety of acoustic sources, detected at great distances by modern, sonar equipment. The task is made difficult by the fact that single acoustic sources have a complex spectrum consisting of several base frequency components and related harmonics. The task for operators is to analyse the data to determine if there is a pattern that represents the signature of a known source, thereby leading to identification of a vessel. Since the task can be highly labour intensive, automated decision aids may be of value to the operator, but their effects on performance and design trade-off decisions are not easily predicted or intuitively obvious. The task network model provided a means for developing a baseline system, against which the performance advantages of various decision aids could be evaluated. The specific improvement in performance predicted by the model for one promising aid was then validated experimentally.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2006
- Accession Number
- ADA634389
Entities
People
- Jacquelyn M. Crebolder
- Jeff Bos
- Michael L. Matthews
- Sharon Mcfadden
Organizations
- Defence Research and Development Canada