Multi-Agent System for Mission and Situational Awareness Management (MASAM) for Airborne Platforms

摘要

This paper will describe the application of improved methods of sorting, classifying, and identifying pulsed and modulated CW command and control (C2) emitters, whether air, land, sea, or subsurface. These methods are based on Artificial Intelligence (AI) and include: (1) Probabilistic reasoning techniques such as Bayesian and/or Dempster-Shafer theory for data fusion and analysis; (2) Genetic algorithms for multi-dimensional search and optimization (i.e. to search for optimized maneuvering and threat-response tactics); (3) Fuzzy set theory, which can be implemented with various other AI techniques. For example, fuzzy logic controllers may be coupled to genetic algorithms and knowledge based rule-generating systems to provide adaptive control mechanisms; (4) Neural networks, to aid in solving many pattern recognition, decision- making, data fusion and analysis problems. We will then discuss the integration of the results from these techniques with software agents, to support the capability to continuously manage the influx of on-board sensor data with received off-board information to maintain situational awareness (the relationship between own-platform and all other observed platforms in the context of the common physical environment). Software agents are characterized as autonomous, intelligent, collaborative, and perhaps exhibit some degree of mobility, acting on a user's behalf in order to meet specific objectives. Lastly, we will define the concept of a mission management system that uses the developed situational awareness to perform targeting, avoidance, and denial (countermeasures) functions of own-platform against other-platforms, while assessing own-platform states such as weapon loads and electromagnetic combat capability. A further function of the mission management system would be to specify ingress/egress routing.