Causal Univariate Spatial-Temporal Autoregressive Moving Averages (STARMA) Modelling of Target Information to Generate Tasking of a World-Wide Sensor System
Abstract
The Department of Defense employs a resource limited world-wide sensor system to detect certain events of interest. The purpose of this research was to establish a methodology using a univariate causal STARMA model for forecasting the relative probability of an event occurring in a geographical location during a time block of the day. These relative probabilities are used as input for a tasking model that assigns the scarce sensor resources so as to optimize the detection of these events. The STARMA model is appropriate for forecasting the relative probabilities because a definite temporal relationship and a definite spatial relationship exists in the data bases. The model created is a univariate causal STARMA model in that it only produces forecasts for one of the twenty-two given geographical regions. A causal univariate STARMA model was created to provide forecasts for one event type occurring at region 11 and appears to provide good forecasts. The model is both correlative and causal. The model is correlative in that it uses temporal and spatial correlations to develop the forecasts. The model is also causal in that it employs predictions from an analytical model.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 1992
- Accession Number
- ADA248109
Entities
People
- Kelly A. Greene
Organizations
- Air Force Institute of Technology