Characterization of situation awareness performance

Abstract

Situation awareness (SE) reporting and distribution on the battlefield is the one of the most important drivers of digitizing the battlefield. SA data include all friendly platform positions and known enemy positions from all relevant sources. Each platform communicates its position/status to a position server that disseminates the positions to others. SA data are extremely time dependent; thus, they must be timely processed in order to reduce fratricide on the battlefield. It is, therefore, important to understand the nature of the SA reporting and distribution problem and to determine the performance impact from various design parameters. The paper to develops an analytical approach to characterize the performance of situation awareness (SA) reporting so as to conduct detailed design tradeoffs. A closed-form expression is derived to characterize the average age of SA reports, a measure relating the age of a received SA report to SA reporting intervals, source platform speeds, network delays, and message completion rates. Since the ultimate performance measure is the achievable position error in a tactical environment, the maximally tolerable network delays for a desirable position error are determined in terms of the SA reporting method, message completion rates, and the source platform speed. The derived results can be used to determine estimated position errors and conduct system design tradeoffs under various operating scenarios. Since the position reports, when reported frequently, often contain highly correlated information, a predictive algorithm may be used to reduce the estimated position error and/or to reduce the SA reporting rate. Several predictive schemes such as source prediction and destination prediction methods have been proposed by ITT A/CD to enhance the SA performance. Nevertheless, the analytical approach derived in this paper can be used as a basis to conduct tradeoffs between these predictive algorithms. Some of the results based on the destination prediction method are illustrated for reference.