Automated Algorithm to Detect Changes in Geostationary Satellites Configuration and Cross-Tagging

Abstract

Using broadband and color photometry, analysts can evaluate satellite operational status and affirm its identity. The process of ingesting photometry data and deriving satellite physical characteristics can be undertaken by analysts in batch mode, meaning using an entire batch of data at the conclusion of each observation night, or by automated algorithms in a batch or on-line mode of operation. In an on-line mode, the assessment is generated with each new data point. Tools used for detecting change to satellites status or identity, whether performed with a human in the loop or automated algorithms, are generally not built to detect with minimum latency and traceable confidence intervals. To alleviate those deficiencies, we investigate the use of Hidden Markov Models (HMM), in a Bayesian Network framework, to infer the hidden state (changed or unchanged) of a three-axis stabilized geostationary satellite using broadband and color photometry. Unlike frequentist statistics which exploit only the stationary statistics of the observables in the database, HMM also makes use of the temporal pattern of the observables as well. The algorithm also operates in learning mode to gradually optimize the HMM. Our technique is designed to operate with or without color data. The version that ingests both panchromatic and color data can accommodate gaps in color photometry data. That attribute is important because while color indices, e.g. Johnson R and B, enhance the quality of the belief (probability) of a hidden state, in real world situations, flux data is collected sporadically in an untasked collect, and color data may be absent. Fluxes are measured with experimental error whose effect on the algorithm will be studied. Photometry data in the AFRLs Geo Color Photometry Catalog (GCPC) and Geo Observations with Latitudinal Diversity Simultaneously (GOLDS) data sets are used to simulate configuration changes and identity cross-tags.

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Document Details

Document Type
Technical Report
Publication Date
Oct 18, 2015
Accession Number
AD1001957

Entities

People

  • Phan Dao

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Satellites
  • Bayesian Networks
  • Computational Science
  • Data Sets
  • Databases
  • Detection
  • Detectors
  • Geosynchronous Satellites
  • Hidden Markov Models
  • Information Science
  • Markov Models
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Space Objects
  • Spacecraft

Readers

  • Computer Vision.
  • Neural Network Machine Learning.
  • Space Exploration and Orbital Mechanics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Translation
  • Space
  • Space - Space Objects