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.
Proc. SPIE. 5429, Signal Processing, Sensor Fusion, and Target Recognition XIII
KEYWORDS: Detection and tracking algorithms, Sensors, Linear filtering, Computer simulations, Telecommunications, Data communications, Active sensors, Filtering (signal processing), Data fusion, Algorithms
Networked data fusion applications require adaptive strategies to
maximise their performance subject to fluctuating resource constraints. If the application is simply picture compilation (i.e., target tracking and identification) then Fisher/Shannon metrics provide a normative basis for approaching this problem. In this paper we demonstrate how information gain can be used to manage a constrained communication bandwidth in a decentralised tracking system that has to adapt to asymmetric communication bandwidth and data delays. When the sensor nodes are active participators in the information acquisition process, the relevance of information must also be considered. Specifically, what is the balance between the cost of information and the expected pay-off resulting from its application in a decision-making process? It is described how issues such as this fit into the formal framework of decentralised partially observed Markov decision process (DEC-POMDP) theory.