# Accession Number:

## ADA622270

# Title:

## Propagation of Bayesian Belief for Near-Real Time Statistical Assessment of Geosynchronous Satellite Status Based on Non-Resolved Photometry Data

# Descriptive Note:

## Conference paper

# Corporate Author:

## AIR FORCE RESEARCH LAB KIRTLAND AFB NM SPACE VEHICLES DIRECTORATE

# Personal Author(s):

# Report Date:

## 2014-09-01

# Pagination or Media Count:

## 39.0

# 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-tag202. 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 201member202 that belongs to a geosynchronous cluster group. This provides a sequence of conditional probability values for each member in a group.

# Descriptors:

- *BAYES THEOREM
- *PHOTOMETRY
- *SATELLITE CONSTELLATIONS
- *SYNCHRONOUS SATELLITES
- ALGORITHMS
- ANOMALIES
- BRIGHTNESS
- CLUSTERING
- EARTH ORBITS
- MARKOV PROCESSES
- MATHEMATICAL PREDICTION
- MULTISENSORS
- OBSERVATION
- OPERATIONAL READINESS
- OPTICAL DETECTION
- PROBABILITY DISTRIBUTION FUNCTIONS
- SATELLITE COMMUNICATIONS
- SITUATIONAL AWARENESS
- SPACE BASED
- SPACE MAINTENANCE
- SPACE OBJECTS
- STATISTICAL INFERENCE
- THRESHOLD EFFECTS

# Subject Categories:

- Statistics and Probability
- Optical Detection and Detectors
- Unmanned Spacecraft