Automated all source data fusion primarily fuses analyst generated messages. The Automated Targeting Data Fusion (ATDF) effort created an application that rapidly associated very diverse information sources automatically. In recent military conflict situations, enormous amounts of multi-source data have been made available to war-fighters. These data include imagery, signals intelligence, acoustic information, input from human analysts, and other sources. The
significant magnitude of collected intelligence overwhelms analysts, operators, collection managers and commanders. Complicating matters further is that the data is collected, transmitted, processed, exploited and disseminated via multiple stovepipe architectures involving different types of intelligence and support personnel. It is difficult for diverse users to maintain situational awareness over the battle-space. This is an extremely crucial issue with rapid targeting decisions. This ATDF capability offers an alternative to automatically support each individual user as well as decision
makers who must use all of their collective command-level decisions. ATDF rapidly associates diverse intelligence products as they are collected and fuses it into actionable knowledge in near real-time.
Automated all source data fusion primarily fuses analyst generated messages. These messages represent a small portion, albeit a highly reliable segment, of the available information resources. Task saturation and computational limitations often prevent volumes of raw collection data from reaching an analyst in a timely manner. Valuable intelligence information that at least corroborates an important time critical target may be present in unanalyzed raw data files.
Analyst generated messages are shown to direct a focused search for additional corroborating evidence in a small spatial-temporal segment of raw data. Automatic corroboration processing simply confirms or denies the presence of a feature in a particular location. Corroboration is a much simpler process than automatic target recognition and requires significantly less processing and fidelity since other information products detect and identify the potential presence of a target or event and focus raw data processing. The new approach transforms previously disregarded raw data into
associated corroborative information without increasing analyst tasking. Existing software fuses the corroborative information with analyst messages. An example demonstrates raw data corroboration using imagery. The approximate location, time, and target identity are determined using two associated analyst messages. Raw imagery processing confirms the target and associates an additional message. The fused target priority is amplified by the corroborative