Information value mapping for fusion architectures

In assessing a fused sensor system, one considers the quality of the system architecture most often by the capabilities of the individual sensors, and the attributes of the fusion algorithm. Though it is possible to evaluate system performance in an idealized context, and model real-world perturbations as random disturbances, it may be advantageous to treat predictable events as deterministic. Additionally, a priori information is not limited to constraints on the object of interest, but also can be applied to the scene in which the object resides. In this paper, the role of information value mapping in the design of efficient fusion architectures is presented. This concept provides a development tool for general scene modeling that attempts to capture the relative value of each sensor´s input related to a given location in the sensed scene. Some general guidelines for the development of information value maps will be outlined. Specific examples illustrate the modeling and implementation methods. The effects of including signal processing methods for modifying the sensor outputs, and hence the information value maps, will be given a particular focus. The use of currently popular image super-resolution algorithms is offered for an illustrative discussion of these methods.