Propagation of Bayesian Belief for Near-Real Time Statistical Assessment of Geosynchronous Satellite Status Based on Non-Resolved Photometry Data
Abstract
The objective of Bayesian belief propagation in this paper is to perform an interactive status assessment of geosynchronous satellites as each new data point for the photometric brightness becomes available during the synoptic search performed by a space-based sensor as a part of its routine metric mission. The calculations are performed by using a dimensionless ratio of observed photometric brightness to its predicted brightness. The brightness predictions can be obtained using any analytical model chosen by the user. The inference for a level of confidence in the statistical assessment is performed on the basis of propagated values for belief within a cluster of satellites that are located within a close proximity to each other. This is meant to render the assessment to be as independent of assumptions and algorithms utilized in the analytical model as possible; and to mitigate the effect of bias that could be introduced by the choice of analytical model. It considers that a model performs predictions based on the geometry of observation conditions and any information that could have been extracted by the inversion of prior data on its photometric brightness. Thus, if there is a statistical change in the predictive error for a single satellite or a pair of satellites, while remaining unchanged for the rest, there is higher likelihood of anomaly in either the operational status of that satellite or an error in object correlation \201i.e. cross-tag\202. The algorithm in this paper uses a first order Markov chain model to compute a conditional probability value for the satellite status to be nominal or anomalous (i.e., NOM or ANOM) given its latest photometry observation. This calculation is repeated as data for each new observation becomes available. Also, it is performed for each satellite \201member\202 that belongs to a geosynchronous cluster (group). This provides a sequence of conditional probability values for each member in a group.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2014
- Accession Number
- ADA622270
Entities
People
- Anil B. Chaudhary
- Jeremy Murray-krezan
- Keith Lucas
- Kimberly K. Kinateder
- Phan Dao
- Tamara Payne
Organizations
- Air Force Research Laboratory