Information theoretic approach to sensor scheduling
Gregory A. McIntyre, Kenneth J. Hintz · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1996
This paper demonstrates an approach to sensor scheduling and sensor management which effectively deals with the search/track decision problem. Every opportunity a sensor has to sense the environment equates to a certain amount of information which can be obtained about the state of the environment. A fundamental question is how to use this potential information to manage a suite of sensors while maximizing one's net knowledge about the state of the environment. The fundamental problem is whether to use one's resources to track targets already in track or to search for new ones. Inherent in this search/track problem is the further decision as to which sensor to use. A computer model has been developed that simulates a modest multiple sensor, multiple threat scenario. Target maneuvers are modeled using the Singer Model for manned maneuvering vehicles. Each sensor's capabilities and characteristics are captured in the model by converting their energy constraints to a probability of detecting a target as a function of range and field of view (beamwidth). The environment is represented by a probability distribution of a target being at a given location. As the environment is sensed and targets are detected, the environment's probability distribution is continually updated to reflect the new probability state of the environment. This probability state represents the system's best estimate about the location of all targets in track and the probable location of, as yet undetected, targets.