A new multi-target tracking algorithm for a large number of orbiting objects

E. D. Delande, Jeremie Houssineau, Jose Franco, C. Frueh, D. E. Clark

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

3 Citations (Scopus)

Abstract

This paper demonstrates the applicability of the filter for Hypothesised and Independent Stochastic Populations (HISP), a multi-target joint detection/tracking algorithm derived from a recent estimation framework for stochastic populations, to wide area surveillance scenarios in the context of Space Situational Awareness. Designed for multi-object estimation problems where the data association between targets and collected observations is moderately ambiguous, the HISP filter has a linear complexity with the number of maintained tracks and the number of observations, and is a scalable filtering solution adapted to large-scale target tracking scenarios. It is illustrated on a challenging surveillance problem involving 30 targets on different orbits, observed by 3 sensors with limited coverage, measurement noise, false alarms, and missed detections.

Original languageEnglish
Title of host publicationSpaceflight Mechanics 2017
PublisherUnivelt Inc.
Pages2077-2096
Number of pages20
ISBN (Print)9780877036371
Publication statusPublished - 5 Feb 2017
Event27th AAS/AIAA Space Flight Mechanics Meeting 2017 - San Antonio, United States
Duration: 5 Feb 20179 Feb 2017

Publication series

NameAdvances in Astronautical Sciences
PublisherUnivelt
Volume160
ISSN (Print)0065-3438
ISSN (Electronic)1081-6003

Conference

Conference27th AAS/AIAA Space Flight Mechanics Meeting 2017
CountryUnited States
CitySan Antonio
Period5/02/179/02/17

ASJC Scopus subject areas

  • Aerospace Engineering
  • Space and Planetary Science

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