Global tracking of space debris via CPHD and consensus

Baishen Wei; Brett Nener; Weifeng Liu; Liang Ma

doi:10.1016/j.asr.2017.03.002

Global tracking of space debris via CPHD and consensus

Baishen Wei
, Brett Nener
, Weifeng Liu
, Liang Ma

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

Space debris tracking is of great importance for safe operation of spacecraft. This paper presents an algorithm that achieves global tracking of space debris with a multi-sensor network. The sensor network has unknown and possibly time-varying topology. A consensus algorithm is used to effectively counteract the effects of data incest. Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPHD) filtering is used to estimate the state of the space debris. As an example of the method, 45 clusters of sensors are used to achieve global tracking. The performance of the proposed approach is demonstrated by simulation experiments.

Original language	English
Pages (from-to)	2548-2562
Number of pages	15
Journal	Advances in Space Research
Volume	59
Issue number	10
DOIs	https://doi.org/10.1016/j.asr.2017.03.002
Publication status	Published - 15 May 2017

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SDG 16 Peace, Justice and Strong Institutions

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10.1016/j.asr.2017.03.002

Cite this

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title = "Global tracking of space debris via CPHD and consensus",

abstract = "Space debris tracking is of great importance for safe operation of spacecraft. This paper presents an algorithm that achieves global tracking of space debris with a multi-sensor network. The sensor network has unknown and possibly time-varying topology. A consensus algorithm is used to effectively counteract the effects of data incest. Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPHD) filtering is used to estimate the state of the space debris. As an example of the method, 45 clusters of sensors are used to achieve global tracking. The performance of the proposed approach is demonstrated by simulation experiments.",

keywords = "Consensus, CPHD, Multi-sensor fusion, Space debris, Tracking",

author = "Baishen Wei and Brett Nener and Weifeng Liu and Liang Ma",

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language = "English",

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TY - JOUR

T1 - Global tracking of space debris via CPHD and consensus

AU - Wei, Baishen

AU - Nener, Brett

AU - Liu, Weifeng

AU - Ma, Liang

PY - 2017/5/15

Y1 - 2017/5/15

N2 - Space debris tracking is of great importance for safe operation of spacecraft. This paper presents an algorithm that achieves global tracking of space debris with a multi-sensor network. The sensor network has unknown and possibly time-varying topology. A consensus algorithm is used to effectively counteract the effects of data incest. Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPHD) filtering is used to estimate the state of the space debris. As an example of the method, 45 clusters of sensors are used to achieve global tracking. The performance of the proposed approach is demonstrated by simulation experiments.

AB - Space debris tracking is of great importance for safe operation of spacecraft. This paper presents an algorithm that achieves global tracking of space debris with a multi-sensor network. The sensor network has unknown and possibly time-varying topology. A consensus algorithm is used to effectively counteract the effects of data incest. Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPHD) filtering is used to estimate the state of the space debris. As an example of the method, 45 clusters of sensors are used to achieve global tracking. The performance of the proposed approach is demonstrated by simulation experiments.

KW - Consensus

KW - CPHD

KW - Multi-sensor fusion

KW - Space debris

KW - Tracking

UR - https://www.scopus.com/pages/publications/85016202467

U2 - 10.1016/j.asr.2017.03.002

DO - 10.1016/j.asr.2017.03.002

M3 - Article

AN - SCOPUS:85016202467

SN - 0273-1177

VL - 59

SP - 2548

EP - 2562

JO - Advances in Space Research

JF - Advances in Space Research

IS - 10

ER -

Global tracking of space debris via CPHD and consensus

Abstract

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