Threat modeling for sensor optimization

The Navy and other US government entities (e.g., Drug Enforcement Agency, Homeland Security, Border Patrol) frequently must design and manage distributed sensor fields to search for targets over large areas. Field performance is often based solely on sensor coverage or probability of detection evaluations, which provide only a fleeting snapshot of how the field is performing at a particular time. This paper describes an efficient and more tractable approach to evaluating the performance of distributed sensor fields. Given a priori target probability distributions and sensor measurements distributed in time and space, the approach directly scores entire sensor fields based on their ability to detect threats and process false alarms. It maintains an historical record of areas searched, from which cumulative coverage can be computed. It has been used to manage 2- or 3-dimensional target/threat probability density functions moving through distributed sonar fields, with sensor performance updated to reflect real-time feedback of environmental and field conditions. This work will focus on a marine security and defense scenario.