Fusing and Integrating Hard and Soft Engines (FISHE) for ASW Situational Understanding

Previous efforts to improve Anti-Submarine Warfare situational understanding have focused on developing fusion techniques for improving the tactical picture. These fusion processes primarily operate on"hard"sensor data that is amenable to software implementation. The Navy now seeks to develop tools to improve the ASW operator's understanding of higher level tactical situations, thereby improving Detection-to-Engagement timelines. Developing tools to support situational understanding, however, is a difficult challenge because it requires the inclusion of"soft"text-base data in the fusion process. Perhaps the most important, and overlooked,"soft"information source is the knowledge and experience that operators use to interpret the events associated with each evolving ASW situation. Their actions are actually indicators of the cognitive processes they use to fuse hard and soft information with their own knowledge, in order to reason about the meaning of the events. This"soft fusion"process is a key element of situational understanding. The DECISIVE ANALYTICS/CUBRC team proposes a combination of novel machine learning and fusion technologies that are designed to leverage this soft fusion process by capturing the relationships between operator actions and both hard and soft data sources in the fusion process to provide an information fusion framework for ASW situational understanding.