Sensor Resource Management (or process refinement) is a element of any information fusion system. Common Level 4 sensor management (SM) inter-relations to Level 1 target tracking and identification have been developed in the literature. During Fusion06, a panel discussion was held to explore the challenges and issues pertaining to the interaction between SM and situation and threat assessment. This paper summarizes the key tenants of the discussion to filter vast experiences of the invited panel experts. The common themes were: (1) Addressing the user in system management / control, (2) Determining a standard set of metrics for optimization, (3) Optimizing / evaluating fusion systems to deliver timely information needs, (4) Dynamic updating for planning mission time-horizons, (5) Joint optimization of objective functions at all levels (6) L2/3 situation entity definitions for knowledge discovery, modeling, and information projection (7) Addressing constraints for resource planning and scheduling

This paper serves both as introduction to and motivation for the panel, and as a position paper to highlight retrospectives and perspectives on issues and challenges of levels 2/3 fusion implementations by presenting an independent annotated point of view.

This panel position paper addresses issues and challenges of resource management (facilitating adaptive and automatic information fusion) and identifies the necessary interaction with levels 2 and 3 components of "information fusion". Starting with the definitions of fusion levels, implicit and explicit interactions among the levels are examined from several perspectives: identifying commensurate measures to model interactions; selection of objective functions at each level or globally; representation of optimum decision making under uncertainty; effects of local optimization at each fusion level vs. joint optimization; representation of interactions among fusion levels by the perceptual reasoning machine paradigm-based adaptive anticipatory planning and control model; and identifying research directions

Modern surveillance systems often utilize multiple physically distributed sensors of different types to provide complementary and overlapping coverage on targets. In order to generate target tracks and estimates, the sensor data need to be fused. While a centralized processing approach is theoretically optimal, there are significant advantages in distributing the fusion operations over multiple processing nodes. This paper discusses architectures for distributed fusion, whereby each node processes the data from its own set of sensors and communicates with other nodes to improve on the estimates, The information graph is introduced as a way of modeling information flow in distributed fusion systems and for developing algorithms. Fusion for target tracking involves two main operations: estimation and association. Distributed estimation algorithms based on the information graph are presented for arbitrary fusion architectures and related to linear and nonlinear distributed estimation results. The distributed data association problem is discussed in terms of track-to-track association likelihoods. Distributed versions of two popular tracking approaches (joint probabilistic data association and multiple hypothesis tracking) are then presented, and examples of applications are given.