Accession Number:

ADA248109

Title:

Causal Univariate Spatial-Temporal Autoregressive Moving Averages (STARMA) Modelling of Target Information to Generate Tasking of a World-Wide Sensor System

Descriptive Note:

Master's thesis

Corporate Author:

AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH

Personal Author(s):

Report Date:

1992-03-01

Pagination or Media Count:

229.0

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.

Subject Categories:

  • Target Direction, Range and Position Finding

Distribution Statement:

APPROVED FOR PUBLIC RELEASE