Paper
25 May 2005 Information-based decision making over a data fusion network
Author Affiliations +
Abstract
In a dynamic battlespace the utility of decisions is a sensitive function of their timeliness. The allocation of weapons, and assignment of sensing and communications resources are examples where timely decisions are crucial to long-term survival. In this paper, we investigate the application of information-based decision-making theory to a data fusion network in such an environment. Specifically, we examine the advantages of decentralised information-based control for sensor-to-target assignment for identification as well as weapon allocation, and compare different utilities for optimising the combat strategy and therefore the life of the network. In our application a positive identification is required before a weapon can be allocated to the target. The quality of sensor-to-target assignment is a key factor in determining whether incoming threats can be identified and destroyed before they can act. The two aspects of information-based control in our scenario are sensor-to-target assignment and weapon allocation. These can be treated as sequential decision-making processes, or more optimally as an integrated process. In the sequential approach, the decisions from the sensor-to-target assignment are simply propagated. The integrated approach assigns sensors to targets on the basis of the utility of the information that can be gained in the context of the weapon allocation decisions that must be made. This concept is also known as information value. Both approaches are considered here.
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Felicity Dormon, Valerie Leung, Dave Nicholson, Ellie Siva, and Mark Williams "Information-based decision making over a data fusion network", Proc. SPIE 5809, Signal Processing, Sensor Fusion, and Target Recognition XIV, (25 May 2005); https://doi.org/10.1117/12.604825
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Sensors

Detection and tracking algorithms

Weapons

Data fusion

Algorithm development

Telecommunications

Sensor networks

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