Analysis of information-theoretic initial sensor search method for space situation awareness

Koki Ho, Ryne Beeson, Kento Tomita, Onalli Gunasekara, Andrew J. Sinclair

Research output: Contribution to journalArticlepeer-review


A study analyzes an information-theoretic metric and its approach for initial sensor search and compares its expected performance with the maximum-probability approach. This information-theoretic approach points the sensor at the candidate direction that provides the largest amount of information about the uncertainties of the object state. The Kullback–Leibler (KL) divergence between the prior and posterior distribution can be used to quantify the amount of gained information, including information gained from no-detection measurements. The study formalizes the information-theoretic approach by providing a mathematical interpretation about the relationship between these two approaches and performing simulations to demonstrate their numerical performance.

Original languageAmerican English
Pages (from-to)641-645
Number of pages5
JournalJournal of Guidance, Control, and Dynamics
Issue number3
StatePublished - 2021
Externally publishedYes

ASJC Scopus subject areas

  • Aerospace Engineering
  • Applied Mathematics
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Space and Planetary Science

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