Soldier Decision-Making for Allocation of Intelligence, Surveillance, and Reconnaissance Assets
Abstract
Intelligence, Surveillance, and Reconnaissance (ISR) has been called the hub of 21st century (military) operations. Military doctrine provides guidelines and protocols for ISR, but little is known about Soldier decision-making for the allocation of ISR platforms. To determine if technology may be useful for augmenting Soldier performance with ISR, we assessed the accuracy of decision-making using simulated allocation tasks. Soldiers made decisions by assigning ISR platform sensors to simplified target detection and identification tasks. The objective, or algorithmic accuracy of the decisions were based on the National Imagery Interpretability Reconnaissance Scale (NIIRS), which consists of normative ratings of imagery interpretability by intelligence analysts across varying sensor capabilities (i.e., pixels on the sensor). Algorithmic accuracy was derived from unclassified/open-source information on sensor capabilities based on NIIRS. Soldiers performed the same set of decision-making tasks twice. First, using their own knowledge and experience with ISR and, second, with complete information on sensor capabilities. Decision accuracy was slightly lower in the first set of assignments compared with the second. However, both were below algorithmic accuracy. Results indicate technology for decision aids with ISR allocation may enhance human decisionmaking.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 2014
- Accession Number
- ADA606633
Entities
People
- Alun Preece
- Diego Pizzocaro
- Jonathan Z. Bakdash
Organizations
- United States Army Research Laboratory