Dynamic sensor steering for target search for space situational awareness

Mihir Patel, Andrew J. Sinclair, Hoong Chieh Yeong, Ryne Beeson, Koki Ho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a set of particle clustering-based sensor steering algorithms to search for a target among a large set of candidate orbits in the context of space situational awareness (SSA). The challenge of such an initial target problem is that the uncertainty region of the target is much larger than the sensor’s FOV and that the sensor is not perfect. Existing initial target search methods use particle-based methods to represent the uncertainties and evaluate each particles to determine the sensor pointing at every time step. The proposed algorithms improve the traditional particle-based algorithms by clustering the particles so that we only need to evaluate the cluster centroids to determine the sensor pointing instead of evaluating all particles. Simulations show that this approach can save the computational cost of particle-based methods significantly while achieving a similar initial target search performance compared to the traditional methods. The proposed methods are expected to improve the real-time performance of initial target search.

Original languageEnglish (US)
Title of host publicationAAS/AIAA Astrodynamics Specialist Conference, 2018
EditorsPuneet Singla, Ryan M. Weisman, Belinda G. Marchand, Brandon A. Jones
PublisherUnivelt Inc.
Pages2175-2188
Number of pages14
ISBN (Print)9780877036579
StatePublished - 2018
EventAAS/AIAA Astrodynamics Specialist Conference, 2018 - Snowbird, United States
Duration: Aug 19 2018Aug 23 2018

Publication series

NameAdvances in the Astronautical Sciences
Volume167
ISSN (Print)0065-3438

Conference

ConferenceAAS/AIAA Astrodynamics Specialist Conference, 2018
CountryUnited States
CitySnowbird
Period8/19/188/23/18

ASJC Scopus subject areas

  • Aerospace Engineering
  • Space and Planetary Science

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