As sensors become more specialized, more powerful, and ubiquitous, previous fixed scheduling methods and
even adaptive algorithms become less effective, particularly in a stressing environment in which decisions must be
made as to how to most effectively allocate sensing resources. Managing sensors based on maximizing the expected
information value rate (EIVR) is implemented in our multi-target, distributed, information based sensor management
simulation of a 6-DOF forward air defense (FAD) environment. This generalized approach of maximizing the flow of
valued information into our model of the world which is used by the mission manager to make decisions better solves
the problem of sensor management since rule based systems cease to perform optimally in a non-stationary
environment and their performance does not degrade gracefully. This paper discusses simulation results demonstrating
the benefits and limitation of our information based approach to sensor management. It also details the scenarios,
performance metrics, and results of the comparison.
The nonlinear operation of sensor managers and their non-stationary stochastic environment require the use of simulation
techniques to quantify and verify their behavior. This is particularly evident when comparing different approaches to
sensor management. It is important to consider which performance metrics are of greatest interest and are useful to the
evaluation and comparison of competing designs. The environment, sensors, tracking, and fusion simulation must all be
unbiased in order to provide an even playing field for evaluating and comparing alternative approaches to sensor
management while still having sufficient fidelity to be useful and conclusive. This paper discusses a distributed
simulation environment for the evaluation of an information-based sensor management system developed to detect,
identify, and track targets. The difficulties and solutions to the need for a top level design, accurate data, computational
complexity, required storage, and inter-modular communications are also examined. Much of the simulation has been
written in Matlab under Linux though the design ideas do not necessarily preclude other environments. The paper
concludes with a preliminary comparison of the performance of a conventional rule based sensor management system
with an information based sensor management system.
Previous papers have introduced the concept of goal lattices (GL) and the GMUGLE(tm) software for assisting the user in entering and ordering a set of goals into a goal lattice as well as assigning relative values to them. The previous assumption was that the GL was static and computed the relative values of the search, track, and ID functions for a reconnaissance mission. For more complex missions in a dynamic environment with expected changes in operational mode, the concept of dynamic goals is introduced. Dynamic goals are instantiated from a set of predefined goals along with their interconnection into the preexisting mission GL. This instantiation is done by the platform sensor manager part of the mission manager and represents a concurrent information request which exists until the platform sensor manager uninstantiates it. A representative example of how goal instantiation is implemented is presented.